On Task

On Task

How Our Brain Gets Things Done

David Badre

PRINCETON UNIVERSITY PRESS

PRINCETON AND OXFORD

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Library of Congress Cataloging-in-Publication Data

Names: Badre, David, author.

Title: On task : how our brain gets things done / David Badre.

Description: Princeton : Princeton University Press, [2020] | Includes bibliographical references and index.

Identifiers: LCCN 2020032349 | ISBN 9780691175553 (hardback) | ISBN 9780691212340 (ebook)

Subjects: LCSH: Cognitive neuroscience. | Executive functions (Neuropsychology) | Developmental psychology. | Decision making.

Classification: LCC QP360.5 .B35 2020 | DDC 612.8/233—dc23

LC record available at https://lccn.loc.gov/2020032349

Version 1.0

British Library Cataloging-in-Publication Data is available

Editorial: Hallie Stebbins and Kristen Hop

Production Editorial: Ali Parrington

Jacket/Cover Design: Layla Mac Rory

Production: Jacqueline Poirier

Publicity: Sara Henning-Stout and Katie Lewis

Copyeditor: Barbara Liguori

To my family.

CONTENTS

1. Acknowledgments      ix

2. Chapter 1

3. What Lies in the Gap between Knowledge and Action?      1

4. Chapter 2

5. The Origins of Human Cognitive Control      32

6. Chapter 3

7. The Stability-Flexibility Dilemma      66

8. Chapter 4

9. Hierarchies in the Head      98

10. Chapter 5

11. The Tao of Multitasking      130

12. Chapter 6

13. Stopping      157

14. Chapter 7

15. The Costs and Benefits of Control      186

16. Chapter 8

17. The Information Retrieval Problem      221

18. Chapter 9

19. Cognitive Control over Lifespan      251

20. Chapter 10

21. Postscript: Getting Things Done That Matter      293

22. Notes      301

23. Index      323

ACKNOWLEDGMENTS

Writing this book has been a new and fulfilling experience for me as a scholar and as a scientist. However, a project of this scope is never really completed by oneself. I owe a deep debt of gratitude to the many people who have contributed both directly and indirectly to its completion.

First, I wish to thank the entire team from Princeton University Press. At every step, from editing to production, the book improved from its rough first draft. I’d like to acknowledge Hallie Stebbins, in particular, who stepped in as editor of the book and masterfully shepherded a novice author through the process. Thanks are also due to Alison Kalett, who I am grateful persuaded me to start this adventure in the first place. Next, I wish to acknowledge my former mentors, John Jonides, Anthony Wagner, and Mark D’Esposito; my current and former lab members; my close long-time collaborator Michael Frank; my colleagues Adam Aron and Roshan Cools, who gave me encouraging feedback on early chapter drafts; and many other colleagues and collaborators too numerous to mention by name. These scientists have shaped my thinking about cognitive control over the years, and their ideas and contributions appear in various ways throughout this book. Finally, I give thanks to my family. To my parents, who have always been supportive of my pursuits and who instilled in me from an early age the love of scholarship and curiosity that led me to be a scientist. And to my wife and two boys, who have helped me in countless ways, from reading multiple versions of the text to simply offering their love, confidence, inspiration, and support. For these, I am deeply grateful. Thank you.

On Task

CHAPTER 1

What Lies in the Gap between Knowledge and Action?

There is a lot of mystery in a cup of coffee—not just in the molecular structure of the drink or in the chemical interactions needed for that morning fix or even in the origin of those D-grade beans, though each of these surely contains mysteries in its own right. A cup of coffee is mysterious because scientists don’t really understand how it got there. Someone made the coffee, yes, but we still don’t have a satisfying explanation of how that person’s brain successfully orchestrated the steps needed to make that coffee. When we set a goal, like making coffee, how does our brain plan and execute the particular actions we take to achieve it? In other words, how do we get things done?

Questions like these fascinate me because they lie close to the heart of what it means to be human. Our species has a uniquely powerful capacity to think, plan, and act in productive, often ingenious, ways. Moreover, our mental apparatus for doing things is somehow general purpose; that is, we are able to get things done even if we have never been in that exact situation with that exact goal before. This ability to get things done drives our common notions of intelligence and personality. We hold in high esteem those who achieve ambitious goals, and we pity those who struggle to achieve any. Yet, the exceptional achievements of the Olympic athlete or the brilliant mathematician are not really what this book is about. This book is about that cup of coffee. It is about what you, the reader, did today. Because what you accomplish on your most unremarkable day, no other species can rival, and no robot yet built can emulate. And how you did it remains an enduring mystery that science has only started to unravel.

To illustrate, let’s consider what you might have done this morning. The very first thing you do is decide that your plan to wake up at 5:30 a.m. for a jog was a bit ambitious, so you hit the snooze button on your smartphone. After doing that a couple more times, you finally get up and walk to the kitchen to prepare a cup of coffee, perhaps thinking about that meeting you’ll have later as you walk. You end up in the kitchen on autopilot. Before starting your familiar coffee-making routine, you throw a bagel in the toaster. All at once, you remember your intention to email your sister. You are thinking of buying a house, and you have been meaning to ask her if her mortgage broker was any good. You make a mental note to send that email at your next opportunity. Or maybe you are the sort of person who stops whatever you are doing to pull out your phone and send your message. You know if you don’t send it now, you won’t remember later. When your coffee is done brewing, you turn to locate a clean mug and then also grab a plate for the bagel while you are at the cabinet. You decide to skip the sugar in your coffee today; surely that makes up for the jog you missed. You hear shouting and your children running down the hall toward you arguing loudly. As you watch the door and await the oncoming storm, your fingers idly nudge your coffee mug away from the counter’s edge.

Our routine mental life is simply a marvel of goal management. Catch us at any given moment, and our head is bustling with goals, plans, strategies, and tactics. Goals are coming and going throughout our waking life, and often more than one at a time. Our goals can range from the abstract and open-ended, like buying a house, to the immediate and trifling, like looking for the cream. We frequently reevaluate our goals based on changing circumstances, desires, or maybe an estimate of our own limitations (will we remember to send that email later?). Some goals intrude unpredictably or unwanted. For example, no one plans to wash a mustard stain out of their shirt but we can make room. It is fair to say that the course and tenor of everyday human life is largely defined by our goals and the various actions we undertake to achieve them.

The human brain has the remarkable ability to manage the buzz and hum of all these goals, in order to plan and execute efficient courses of action. Indeed, our brains are so good at this that most of us view the routine of everyday life as just that: routine. No one ever produced a summer blockbuster about a typical morning making coffee. We mostly take our ability to get things done for granted and only notice it on those rare occasions when we struggle or fail. Yet, this ability is actually quite singular and marvelous, and also, unfortunately, quite fragile.

The brain requires its own elaborate class of neural mechanisms devoted to generating plans, keeping track of them, and influencing a cascade of brain states that can link our goals with the correct actions. Scientists refer to these mechanisms and the processes they support as cognitive control or executive function. Though there are some differences in usage and sense between these terms, they generally refer to the same class of mental function. For consistency, I will use the term cognitive control, only because this is the term currently employed by most cognitive neuroscientists, but I am not distinguishing between these labels.

Regardless of what label it goes by, however, cognitive control has been remarkably difficult to define for scientists and lay people alike. As we will see, some of its slipperiness results from our lack of intuition about cognitive control the way we might have about a memory, a percept, or a movement. Rather, cognitive control processes live in the murky spaces between knowledge and action, influencing the translation from the first to the second while not being either one. Yet, cognitive control is a real class of function, separate from knowledge and action, and is supported by its own systems in the brain. Some of the best evidence we have for this comes from the cases of people who have lost cognitive control function owing to brain disease or disorder. In observing these patients we recognize just how devastating the loss of cognitive control can be to the routine course of our lives and even to our image of ourselves as effective agents in our world.

The Functionless Cortex

Cases of brain disease or damage demonstrate the vulnerability of cognitive control, as well as its necessity for success in everyday life. Control function is fragile, as it suffers some degree of loss across many, if not most, neurological and psychiatric disorders, ranging from stroke to Parkinson’s disease to Alzheimer’s disease to autism. In general, the loss of cognitive control most conventionally arises when these diseases and disorders affect the frontal lobes, and specifically, the prefrontal cortex, or PFC (Figure 1.1).

These days it is widely accepted that the prefrontal cortex is crucial for our highest mental functions, including cognitive control. Indeed, this idea has captured the popular imagination. Movies and television police dramas are fond of blaming frontal lobe damage for everything from irrational, violent behavior to personality change. One health website advertises “10 Exercises for your prefrontal cortex”[1] to help you “focus and think” by doing dubious things like “Learn to Juggle.”

In our modern context, then, it might be surprising to learn that at one time neuroscientists wondered if the prefrontal cortex might actually be functionless or, at the very least, so auxiliary in function as to be expendable. In the late nineteenth and early twentieth centuries, neuroscientists started precisely damaging or “lesioning” parts of the brain in animal experiments to discover what function they served. If an animal lost a particular function after a lesion, then it could be inferred that the part of the brain that had been lesioned was necessary for that function. In parallel, clinicians who surgically resected parts of the human brain in the course of a treatment or who saw patients with brain damage from stroke, head injury, or other causes, would try to draw similar inferences from the resulting loss of function. Oddly, however, when the prefrontal cortex was lesioned, these early investigators failed to observe the kinds of dramatic changes one would expect from the loss of higher cognitive function. Indeed, it was surprisingly difficult to define a specific function that was lost at all.

Patients, particularly those with damage affecting only the prefrontal cortex, demonstrated normal sensory and motor function on a neurological exam. They could speak with their doctor in a knowledgeable and articulate way about their past experiences or the topics with which they were familiar, and they often performed quite well on the types of bedside or clinical neuropsychological tests sensitive to mental decline in other patient groups. In his landmark 1890 psychology text, The Principles of Psychology, Harvard psychologist William James spent a chapter reviewing the known functions of the brain at the time, and he captured the perplexed state of the field concerning the prefrontal cortex.[2] He called the frontal lobes a “puzzle” and stated, “The anterior frontal lobes, for example, so far as is yet known, have no definite functions. […] neither stimulation nor excision of the prefrontal lobes produces any symptoms whatever.”

FIGURE 1.1. Drawing of the left lateral surface of the human brain showing the location of the frontal lobe and prefrontal cortex. The compass rose at the bottom provides the direction indicated by relative anatomical terms. For example, the terms anterior or rostral both mean toward the front of the head, and dorsal means toward the top.

The puzzle of the frontal lobe led to a half-century debate among neuroscientists regarding its function. Many thought it might serve no crucial intellectual function at all or, at most, might serve an auxiliary or supporting function. In 1945, legendary neuroscientist Donald Hebb even described a patient who seemed to improve after surgical removal of frontal tissue.[3] This patient’s frontal lobe had been damaged in a sawmill accident. According to Hebb, the patient went from impulsive and unreliable before the surgery to a model citizen after, with maybe only an odd tendency to change jobs every couple of months.[4]

Sadly, it was in part this ambivalence toward prefrontal function that set the stage for the widespread use of frontal lobotomy as a treatment for psychological disorder in the 1950s. After all, if the prefrontal cortex was superfluous, then the subtle cost of its loss was surely outweighed by the potential benefit of relieving major psychiatric symptoms. However, the frontal lobes are not expendable, and tragically, only after some 40,000 estimated lobotomies in the United States alone, and the individual suffering that attended them, was the practice stopped. Outside of the clinic, a different story was evident in the lives of even the earliest cases of prefrontal damage.

In 1928, a 43-year-old woman was referred to the Montreal Neurological Institute because of recurring seizures she had periodically experienced since about the age of 20. She reported to her doctors that she was “mentally not up to her own standard in looking after household arrangements.” Among her doctors was Wilder Penfield, the brilliant pioneering neurosurgeon. Penfield had revolutionized treatment for epilepsy with his “Montreal Method” of electrically stimulating an awake patient’s brain during surgery, providing a means of locating seizure foci to remove, as well as functional cortex to avoid. However, this occasion and this patient were far from usual. This patient was Penfield’s only sister.

Upon removing her cranium during surgery, her doctors discovered a large tumor located in the right frontal lobe. The tumor had infiltrated a wide area, and to excise it, the surgeon had to remove almost the entire right frontal lobe, up to about a centimeter before the motor cortex. Penfield’s sister was discharged following recovery from the surgery, and she tried to return to her 1920s lifestyle as a wife and mother of six. Tragically, due to regrowth of the tumor, Penfield’s sister died about two years after first being seen at the MNI.

Though conflicted because of his deep personal involvement, Penfield decided to report her case in the medical literature.[5] He felt that the details of her case might be uniquely important to posterity.

One of the early obstacles to understanding the kinds of problems that patients were experiencing following frontal lobe damage was a lack of baseline reference. Patients would be referred to doctors when there was already a problem, perhaps after years of tumor growth, for example, and so observations of the patient could not easily be compared with their behavior prior to the brain disorder. In this case, however, Penfield had known his sister his entire life. He was able to comment in personal and sometimes intimate prose about the changes he saw. The picture he painted was of a woman who retained a pleasant disposition along with much of her core intellectual function, yet who became lost in the everyday activities she had easily performed years earlier.

Even following removal of a substantial amount of frontal tissue, Penfield’s sister impressed the doctors attending her with her manner and courtesy. Dr. Colin Russel, who had examined her, noted the following interaction to Penfield in a letter:

[S]he expressed her appreciation of what she considered my kindness in giving up my time [to see her], so perfectly and with so much courtesy that it was really very impressive. She said that she had felt so afraid of causing you distress by making an exhibition of herself and that I had helped her. When I remarked that the only exhibition I had seen was one of the best exhibitions of courage that it had been my fortune to witness, she expressed her gratitude so nicely that one could not help wondering how much the frontal lobe had to do with the higher association processes.

Professor Russel was likewise impressed by her intellectual function, such as the quality of her personal memories or her ability to discuss with him books she was reading. None of this sounded like a person who had experienced much loss of function following such a large removal of frontal tissue.

Her own take on matters was quite different, however. She described in one of her letters to Penfield that “Dr. Taylor asked me if I felt that mental activity was improving, and I said ‘Yes,’ but it seems as though each time I feel encouraged that way, I do a series of very stupid things [emphasis mine].” The intellectual loss she felt was not experienced as an overt loss of knowledge or an inability to function at all. Rather, in her words, she was frustrated by frequent failures to perform the complex but routine activities that are the fabric of everyday life.

For example, on one occasion, she invited Penfield and four other guests over for dinner. Penfield described her distress upon his arrival.

She looked forward to it with pleasure and had the whole day for preparation. This was a thing she could have done with ease ten years before. When the appointed hour arrived she was in the kitchen, the food was all there, one or two things were on the stove, but the salad was not ready, the meat had not been started and she was distressed and confused by her long continued effort alone. It seemed evident that she would never be able to get everything ready at once.

She had managed to get all the food out and maybe even had some items bubbling away. But the full meal, as a concerted act, would never have been completed had she been left to do so independently. As anyone knows who has prepared a large meal for several people, you don’t just cook each dish on its own and then set it aside to work on the next. Rather, you coordinate multiple preparations at once to produce a coherent whole. You pause headway on one dish to move to another. You manage bottlenecks in the kitchen. You make progress checks. And the various components of the meal must all be finished at roughly the same time, with none too hot and none too cold. This is a challenge even for the experienced amateur cook. It is insurmountable for the patient without cognitive control, as it was for Penfield’s sister.

Injury to our cognitive control system results in real, qualitative loss. It becomes challenging to get things done. For Penfield’s sister, making a meal for a dinner party was as impenetrable as forming a new memory is for an amnesic patient or speaking is for an aphasic patient. Yet, she described this experience not as a performance issue but as an intellectual loss, as feeling “stupid.”

It is important to clarify that the mental decline she expressed is not necessarily related to a loss of core knowledge itself, in the colloquial sense of the word, as in our stored collection of facts, experiences, beliefs, and our conscious understanding of the world about us. Rather, problems with cognitive control can be present in patients whose knowledge base appears entirely normal or even better than normal.

This paradox was dramatically demonstrated in the case of another patient, called EVR, who was reported by Paul Eslinger and the neurologist and neuroscientist Antonio Damasio in 1986.[6] EVR was a 44-year-old accountant who had undergone surgery to remove a frontal tumor around a decade prior to Damasio’s report and with the surgery had lost a large portion of his medial and ventral frontal lobes. On multiple tests he was given in the clinic, EVR consistently displayed a superior intellect, even a decade after his surgery. He performed at the highest levels (97–99th percentile) on the Wechsler Adult Intelligence Scale, or WAIS, a widely used intelligence test. In conversation, he drew on a rich knowledge base that he exhibited freely, for instance, offering depth and insight about Reagan’s neofederalist political philosophy (this was the ’80s, after all). He even performed at normal levels on conventional neuropsychological tests intended to detect frontal lobe dysfunction.

While EVR was acing all the doctors’ tests, however, his life outside the clinic was a mess. Prior to his brain damage, EVR had been a respectable married father of two, was active in his local church, and had risen to the level of comptroller at a construction firm. But as the tumor grew, compressing his frontal lobes, and following the surgery to remove it, he had undergone a transformation.

EVR had returned to work after his surgery but eventually lost this job and several subsequent ones because he was repeatedly late and disorganized. He lost his life savings in a risky business scheme with a questionable partner. His wife of seventeen years divorced him and took their two children with her. He remarried, divorced, and remarried again. At the time of Damasio’s report, EVR was living with his parents and concocting wild money-making schemes, which he never saw through.

In his day-to-day life, EVR was similarly ineffective. For example, he might take two hours trying to get ready in the morning. And he could spend whole days just concerned with washing his hair. A plan to go to out in the evening would never come to fruition. He would initiate, but ultimately abort, multiple visits to candidate restaurants, or read and repeatedly reread their menus. EVR could express a larger goal, even a rough plan for the future, like eating out at a restaurant, but he could not plan the actions to realize that goal. It was the organization and management of the shorter- to intermediate-term goals that eluded him. EVR found himself stuck—with an image of an endpoint but bewildered by the options, overwhelmed by the paths that he might take to get there, or lacking the initiative to start culling them.

We are perhaps starting to understand why neurologists and neuroscientists had a hard time pinpointing what function had been lost with frontal lobe damage. Like EVR or Penfield’s sister, frontal patients were fluent in conversation, knowledgeable, and competent in their moment-to-moment interactions. Further, many of the problems these patients exhibited in their lives might be seen as annoying personality traits in lots of perfectly healthy people. In his review of frontal lobe case studies, behavioral neurologist Frank Benson compiled several descriptive terms commonly used by neurologists in describing frontal cases.[7] His table of “Frontal Lobishness,” reproduced as Box 1.1, includes such traits as puerility, boastfulness, or lewd conversation, which might also be characteristic of that annoying person who corners you at a party. The difference is that these traits manifest consistently and aberrantly with frontal lobe disorder and often reflect a change in that person’s personality.

As with personality traits, it was hard to distinguish which adverse life events or failures of enterprise were precipitated by brain damage versus which might just be bad luck or follow from some eccentricity or intemperance of personality. Making foolish business decisions, lacking punctuality, changing jobs frequently, or being married and divorced multiple times are certainly not, in and of themselves, diagnostic signs of frontal lobe disorder. However, causes of these life events and their exaggerated frequency following damage to the frontal lobe suggested that one or more functions were being compromised in these patients that made them unusually susceptible to this pattern.

Modern clinical studies confirm in large groups of patients what these case studies illustrated in their anecdotes. On average, frontal patients in these studies exhibit worse job and scholastic performance and are unable to manage their households or themselves. They often get into financial or legal trouble, and many end up hospitalized or in care of others because of their inability to complete the basic tasks of daily life.[8]

Box 1.1. Benson’s Manifestations of Prefrontal Damage (Frontal Lobishness)

Tastelessness

Irritability

Poorly restrained behavior

Disinhibition

Decreased social concern

Coarseness

Jocularity

Hyperkinetic

Facetiousness

Hypokinetic

Witzelsucht [inappropriate joking]

Flare with anger

Moria [foolish euphoria, not place in Middle Earth]

Puerile (silly) attitude

Boastfulness

Disinhibition of social graces

Grandiosity

Inappropriate sexual advances

Decreased initiative

Sexual exhibitionism

Decreased attentiveness

Lewd conversation

Forgetfulness

Erotic behavior

Poor memory

Euphoria

Indifference

Poor planning ability

Apathy

Diminished concern for the future

Shallow effect [sic]

Capriciousness

Lack of spontaneity

Loss of abstract attitude

Abulia [slowing of mind, motor, and speech]

Loss of esthetic sense

Asthenia [lack of energy]

Impulsiveness

Akinesia [loss of motor spontaneity]

Distractibility

Deterioration of work quality

Stimulus bound

Depression

Concreteness

Morose discontent

Perseveration

Restlessness

Delusions:

Grandiosity (strength, wealth, intelligence)

Nihilism

Paranoia

Hypochondriasis

Adapted from Table 11.1 in D. F. Benson, The Neurology of Thinking. (New York: Oxford University Press.)

Nonetheless, these problems in patients’ everyday lives are often missed by scientists and clinicians and the measures they have available to assess them. Indeed, there is still no widely accepted gold standard measure of cognitive control.[9] Some studies have found that the most widely used measures we have explain as little as 18%–20% of the variation among patients in the problems they experience in their everyday lives.[10]

Why are these tests such poor predictors of real world performance? There are several reasons, but one clear problem is that most laboratory tests are too simple. They lack the open-ended complexity we all confront in planning and executing actions in the real world. A 1991 study by neuropsychologists Tim Shallice and Paul Burgess put this disconnect into sharp focus.[11] Three patients with damage to the frontal lobes from traumatic head injury were included in the study, along with a group of healthy people as controls. All three patients performed well on standardized tests of intelligence and cognitive control. However, all three patients also exhibited problems with cognitive control in their everyday lives. For example, one of the patients had at one point excused himself from a therapy session to get a cup of coffee, disappeared, and was later found on the golf course.

The patients and controls were asked to complete a set of errands by themselves around London. They were given some money to budget and various tasks to perform, like buying a loaf of bread or finding what yesterday’s weather had been. The study asked not only whether the patients could do these errands but also how efficiently and correctly they would go about conducting them. For instance, you or I might choose a shop where we can buy two items on the same visit rather than going to two different stores. The investigators hypothesized that the patients would have trouble with this kind of efficient planning. To test their hypothesis, they developed a sophisticated analysis scheme by which they could assign a numeric score not just to how many tasks were completed but also to how efficiently they were carried out and how many rules were broken along the way.

In the end, however, the researchers probably didn’t even need their fancy analysis scheme to see the outcome. Two of the three patients barely completed half of the eight tasks they were given. Controls barely missed one. The single patient who managed to complete all the errands did so inefficiently and broke several rules along the way. For example, this patient successfully found a previous day’s newspaper to determine the weather, but he was then chased down by the store clerk for failing to pay for it.

Perhaps the patients just didn’t know what they were supposed to do and that’s why they failed to complete all those tasks? Nope. Shallice and Burgess included a score for failures of interpretation, and none of the patients differed from controls in these types of errors. They knew what their goals were. Rather, their failures were due to inefficiency, rule breaking, or failing to reach those goals.

To summarize, then, damage to the brain’s cognitive control system can result in a deficit in efficiently and cleanly getting things done. This deficit can be as dense and as devastating as any other loss of function seen in other patient groups. The tricky part for the clinician and scientist is that cognitive control is built to help us achieve our goals in the complexity of the world outside the laboratory. This is its niche, and so it is in this setting that control is needed and thus where deficits are consistently observed.

The Gap between Knowledge and Action

Why do these frontal lobe patients fail at the basic tasks of everyday life? What has been lost? Answering these questions is difficult. There is likely no such thing as a single, uniform “dysexecutive syndrome,” or loss of cognitive control as a single whole. Rather, as we shall discuss over the course of this book, cognitive control is a complex system with lots of moving parts, a fault in any one of which can affect our ability to get things done in numerous ways. Thus, two patients might show the same failures on a task for entirely different reasons. However, whatever the particular reason, these patients have in common an inability to connect knowledge and action. Of course, this statement implies there is something to lose that is neither knowledge nor action itself but, rather, is a special class of thing that lies between these two endpoints. Is there any evidence that such a thing exists?

First, it is clear that knowing is not sufficient to acting appropriately. We often experience this disconnect when we try to communicate our ideas to others. We know what we want to say or what we’d like to write, but we just can’t find the right words to get the point across. Language is probably not exceptional in this regard. We experience a similar disconnect between more general goals and intentions and the actions that realize them. For example, I have a notion to make that recipe for parsnip soup I saw online, and a fairly firm idea of what it will look and taste like if I do, but I have some serious mental work left to do to actually make one.

Thus, even with the best-formed image of the world, the clearest awareness of a rule for action, and the most urgent desire to achieve an outcome, the brain still requires a way to implement that knowledge. There must be a means to translate an abstract goal into the intricate, moment-to-moment sequencing of behavior.

It follows, then, that intact knowledge and intention are never enough to ensure intact action. Indeed, frontal patients will often be able to state the rules for a task explicitly, and yet, under certain circumstances, they will be unable to follow those very rules.

The neuropsychologist Brenda Milner first noted this paradox in a 1964 study of 71 frontal patients, citing what she termed a “curious dissociation” between her patients’ ability to verbalize the rules for the tests she was administering and their inability to follow those rules.[12] Her patients were performing the Wisconsin Card Sorting Test. In this test, the patient sorts cards with some number of colored shapes on them into piles based on the shape, color, or number of shapes printed on the card. So, for example, a patient sorting according to color would make separate piles for red, blue, or yellow shapes, without regard to the identity of the shapes or their number.

The patient is not told the correct sorting rule, and all three of these features are printed on every card. So, the patient has to identify the appropriate sorting rule based on feedback from the tester. For example, if the rule is to sort by shape, and the patient starts sorting by color, the experimenter tells them “incorrect.” The patient then shifts to sort by shape, to which the tester says “correct.” If they learn, the patient will then continue to sort by shape. Typically, frontal patients are able to figure out the first sorting rule just as quickly as healthy people. However, once the patient has figured out the rule and sorted for a while, the tester changes the rule without telling the patient. So, quite unexpectedly, the patient starts hearing “incorrect” from the tester when they sort based on the rule that had been working until that point.

Healthy people adjust to this change fairly quickly and shift their sorting to find the new rule within a few cards or so. By contrast, frontal patients continue to sort based on the old rule, even while being told “incorrect” over and over. The neuropsychologist’s jargon for this inability to stop a previously valid but now obsolete behavior is perseveration. What Milner noticed, however, is that some patients who perseverate would also comment with growing frustration on their accumulating errors, all the while continuing to follow the old rule again and again. The patients knew the rule for the task, stating spontaneously, “it has to be the color, the form, or the number.” They knew that they were taking the wrong actions. They even knew the tester was going to tell them “incorrect.” Yet, they were simply unable to use this knowledge to guide how they behaved: to stop sorting based on the old rule.

In his rich set of short case examples, Frank Benson provided an anecdote that illustrates this phenomenon in more everyday behavior of a patient with frontal damage:

While being evaluated for the presence of diabetes insipidus, the patient was instructed, “Don’t drink any water; don’t go near the water fountain.” Within a few minutes he was observed having a drink at the water fountain. When asked by the examiner what he had just been told, he immediately replied: “Don’t drink any water; don’t go near the water fountain.”[13]

Of course, one doesn’t need to be a frontal patient to see this disconnect. How many parents have experienced the following interaction with their toddler?

“Maria, please don’t touch that outlet.”

“Okay, Mama.” Touches outlet.

“Maria! What did I just say?”

“Don’t touch the outlet.”

Sound familiar? We commonly interpret these interactions as disobedience or an assertion of independence on the part of a child. Though this might sometimes be the case, this interpretation implicitly assumes that if the child knows the rule, to the point of being able to state it, then she should be able to follow that rule if she wants to. The knowledge is there, so just follow the rule, thinks the exasperated parent. However, a child’s brain is still developing, and on some occasions, they may not lack the knowledge of a rule or even the will to follow it. Rather, they might lack the mature cognitive control processes needed to implement it.

We routinely overlook this gap when interpreting our own or others’ apparent failures to get something done. “If only Uncle Joe knew how unhealthy soft drinks are, he would stop drinking three a day. Let’s email him another article about diabetes.” It might be that Uncle Joe somehow missed the public education campaign regarding the health risks of sugar and obesity, but it may also be that Uncle Joe has not been able to structure his life to act on this knowledge in a sustainable way. In sum, knowing may indeed be half the battle, but the other half is evidently not to be trifled with.

On the other end, cognitive control is also sometimes confused with action itself. But, as with knowledge, intact cognitive control is not required to simply execute intact actions, even complex ones. Consider the following bedside test used by neurologists to identify a potential frontal lobe disorder.[14] A pair of glasses is placed in front of the patient. The neurologist then makes a simple motion to her face, as though putting on a pair of glasses. Upon seeing this, the frontal patient will take the glasses from the table and put them on. This is termed imitation behavior, and it demonstrates that the action of picking up and putting on a pair of glasses is intact. It is held somewhere in the brain as a precompiled routine, and it can be elicited given the right trigger in the environment. Once triggered, the action can arise without regard to the broader situation. The patient can’t stop the action from being triggered by the neurologist’s gesture just because those glasses might not belong to them, or they don’t need them, or, indeed, because they are already wearing their own pair.

Psychologists sometimes refer to these types of actions as “automatic.” They are the actions we can do without thinking about them. They are fast, effortless, and obligatory once triggered. Akin to a habit, they are often performed regardless of our goals, plans, or the broader context of our behavior. Thus, given the right trigger, we can occasionally perform automatic actions without meaning to do so. If you’d like to see an example of this, just search for “Senator Hatch removes glasses that aren’t there” on your web browser.

Automatic actions are not limited to simple behaviors like putting on a pair of glasses. In a series of cases, neurologist F. Lhermitte documented several striking examples of what he called “utilization behavior” and “environmental dependency syndrome.”[15] Lhermitte’s patients performed complex and lengthy action routines that were triggered by objects or cues in the environment, even when these were entirely inappropriate given the broader circumstances.

For example, one patient was brought to Dr. Lhermitte’s house. On entering a bedroom and seeing the sheets pulled back on the bed, the patient proceeded to disrobe down to his underwear and get in the bed. In this case, the patient had no trouble executing the complex sequence of actions needed to get ready for bed. However, doing so was also obviously inappropriate merely because he was in a bedroom and saw a bed. There are some strong social norms and conditions that must be satisfied before we can just hop into someone else’s bed, much less the bed of our neurologist. To be clear, however, this patient was not deluded about where he was or the nature of the broader circumstances or the correct rules for behavior. If you asked him, he could likely tell you he was in his neurologist’s home and that this was not his own bed. Similarly, he would likely confirm for you that it is socially inappropriate to jump into just any bed one might encounter. So what happened here?

We can probably assume that this sequence of actions was a nightly ritual for this man, one that countless bedtimes had hardwired into his brain. Because of his brain damage, however, this patient was unable to use the broader situation to overrule his strong association between beds and getting into them. While automatic actions do not require cognitive control to be initiated, they do require cognitive control to be regulated. Cognitive control allows us to take our knowledge of the broader situation into consideration when deciding things like when we get ready for bed.

When I discuss examples like this in lectures, I am often asked, how can this happen? It’s one thing to remove your invisible glasses in a Senate hearing, but it is quite another to go through a lengthy process of undressing and getting into a bed even knowing that doing so is not correct. The patient is conscious after all. But then again, we all act like a frontal patient occasionally. Have you ever accidentally put the milk in the cabinet and the can of soup in the refrigerator? Have you ever missed the turn to your friend’s house because going straight was part of your more regular route to work? Even William James writing in 1890 made this point in a delightfully dated way in his discussion of the power of habits to drive our behavior:

Who is there that has never wound up his watch on taking off his waistcoat in the daytime, or taken his latch-key out on arriving at the door-step of a friend? Very absent-minded persons in going to their bedroom to dress for dinner have been known to take off one garment after another and finally to get into bed, merely because that was the habitual issue of the first few movements when performed at a later hour.[16]

Most of us are not winding our watches or dressing for dinner anymore, but we know exactly what James means. Indeed, the absentminded person in James’s example behaves much like our patient, compelled by the habit of this setting to carry out a complex sequence of actions. But the difference is that a healthy person can, if properly focused, wrest control over their automatic actions. The frontal patient is incapable of doing so.

There is a significant gap between knowledge and action the brain must bridge to achieve our goals. Intact knowledge does not guarantee aligned action. Rather, cognitive control processes are required to put it all together—to plan, select, sequence, and monitor actions with knowledge in mind. Further, cognitive control is its own class of mental function, distinct from knowledge and action that is required to bridge this gap. So, what does a cognitive control process look like? We will answer this question in the next section.

Bridging the Gap with Control Processes

So far, we have seen there is a substantive gap between knowledge and action. The fact that just knowing about something is not enough to act on this knowledge is neither obvious nor theoretically convenient, and, indeed, it also eluded psychologists for years. Rather, the contrary assumption—that a direct line links an input to the human brain and its appropriate output as behavior—had been fundamental to many psychologists’ thinking at least as early as William James. And this way of thinking continued through the major schools of psychology that dominated the field in the early part of the twentieth century.

It was not until we started building thinking machines ourselves, namely, the modern computer, that cognitive psychologists recognized that control structures were necessary for such a machine to do much of anything. The computer gave psychologists an example of an executive agent that could control itself, and it provided a first example of what a control process might look like: the control flow of a computer program.

Your computer, tablet, smartphone, car, coffee maker, washing machine, and everything with a computer chip in it, which is most things these days, all operate on the basis of programs. Programs are essentially lists of instructions that tell the computer what to do. These lists can be quite massive. For example, a simple iPhone app might have around 10,000 lines of code, the Mac OSX Tiger operating system has more than 80 million, and all of Google’s Internet services are estimated at close to 2 billion. These programs are so long because computers are very literal. They won’t get the gist of what you want them to do. For a computer to do anything, commands must be exact and explicit. Thus, programming computers made clear to the psychologist just how hard it is to get from what you know to what you actually do.

To illustrate, imagine I want the computer to add together any two numbers I give it. I might give it the following instructions:

Input first number

Assign first number to X

Input second number

Assign second number to Y

Look up X + Y in stored addition table

Assign the result to variable ANSWER

Output the value of ANSWER

End program

Now, I’m expressing all this in what programmers would call “pseudocode,” meaning a string of program-like commands that are not in the true syntax of a computer language, like C++ or Python. Real coding would be different and even more explicit. Nonetheless, my pseudocode is enough to illustrate how control flow allows a computer to get tasks done. This little program can be used for adding any two numbers I wish to assign to X and Y and for which I have an entry stored in my computer’s lookup table.

It would be annoying to write and rewrite these instructions every time I wanted to add two numbers. A useful trick for a common task like this is to store it as an individual subroutine to be called as needed by other programs. Now that I have my little program for adding two numbers, I might store it as a subroutine called “add_2” and call it anytime I want to add two numbers.

Importantly, however, to do most tasks, I can’t call subroutines in just any order. Rather, they must be called at particular times and often under particular conditions. For example, what if I wanted my program to add the two numbers only if I am doing a task called “cash register”? I could use a control process: an “if” statement. For example, I might write the code:

Input task from user

Assign task to variable called TASK

If the value of TASK is “cash register”

Then run add_2

Otherwise

Output “I can’t do that task!”

End program

If the user tells the computer the task is to be a cash register, now my little program will execute its add_2 subroutine. For any other task, it will just complain. This is an example of a branching control structure. Similar to encountering a fork in the road, the program will follow one branch under one situation and another otherwise.

Directing control flow can allow the computer to perform substantially more complex tasks. For instance, our cash register program is pretty crummy so far, as it can total only two items at checkout. If you want any more items, you’d need to get back in line. So, instead, we should want our cash register to take the prices of a series of items of any length and given in any order and add them together. To do this, I might add another control structure, a “loop,” that allows me to iteratively call my add_2 subroutine, adding each new item price to a growing total until there are no further items to add.

The computer has a memory with stored knowledge, in this case the addition tables. It has various devices, like keyboards and touchpads to take input, and screens and speakers for output. It also has lots of operations and subroutines, like add_2, which might be analogous to our automatic action routines. But to make the computer do something, like be a cash register, control has to be passed from one subroutine to the other at the right time and under the right conditions. Control determines whether an operation is relevant, how long to execute it, and whether to stop. Strictly speaking, these control commands are not doing the task themselves. The branching and looping control structures are not adding the numbers or outputting the results. Rather, the control processes guide the flow of processing in the service of an overall computational goal. This control of flow is necessary to get the smaller parts working together in the right way to do a task that is greater than the sum of those parts.

Cognitive psychologists seized on the computer program and its control structures as a guiding analogy for human cognitive control. Prominent among these early ideas was the Test-Operate-Test-Exit structure—or TOTE—proposed by George Miller, Eugene Galanter, and Karl Pribram.[17] TOTE sought to describe what a control structure for human behavior might look like and provides a helpful illustration of several features of any cognitive control system. The basic TOTE structure is illustrated in Figure 1.2A.

FIGURE 1.2. Schematic of the Miller, Galanter, and Pribram (1960) Test-Operate-Test-Exit (TOTE) structure. (A) The basic TOTE with arrows showing flow of control from Test to Operate to Test to Exit. (B) A simple TOTE example for eating a banana. (C) Hierarchical elaboration of the TOTE control flow for eating a banana. The operator “Eat Banana” is now represented by flow among three sub-TOTEs. The Chew operator of the middle sub-TOTE is further elaborated as two sub-sub-TOTEs that control the jaw.

A TOTE is essentially a loop applied to human behavior. There is an initial “Test” phase, during which an input to the system is compared with a particular condition. If that condition is not met, this incongruity between the desired condition and the input drives an “Operation” to be performed. Following that operation, there is another “Test.” If the incongruity still exists, then the operation is performed again, and so forth, until the test condition is met, at which point the loop “Exits,” passing control to the next TOTE.

To see how this control system might operate, consider a possible TOTE structure for eating a banana, illustrated in Figure 1.2B. First, we need a test; we’ll call it Test Satiation. If we are hungry, then the incongruence of our hungry state with our satiated test condition drives the operation “Eat Banana.” In this loop, we will continually eat a banana until we are satiated. At that point, when we Test Satiation again, we will exit the TOTE. We have a simple control structure that determines when and for how long we eat a banana.

Of course, the TOTE structure is an oversimplification. This is likely not the true description of the plan for eating a banana that is implemented in our brain, and no current psychological theory relies on TOTEs as Miller, Galanter, and Pribram described them. But even in its simplicity, the TOTE framework illustrates some key points about the basic structure of a control system.

First, the TOTE structure makes clear why knowing that you want to do something is not enough. Doing so entails elaborating a plan for action that has to be built, for example, around organizing and updating test conditions and directing control flow to the right operations, like eating a banana, at the right times. These conditions are not necessarily expressed in the initial goal, and they might change from one situation to the next. Thus, there must be some kind of control flow to structure behavior.

Second, the TOTE incorporates the concept of both conditional testing and feedback as a means of dynamically governing control flow. In other words, operators are selected based on a condition of the world. To the degree that this condition changes as a consequence of the actions of that operator, then this condition is a way of controlling the operator. As engineers know, incorporating feedback as a way of regulating a system is a powerful means of control.

Third, TOTEs make explicit the importance of incorporating a stop rule that determines when to cease doing something so control can be passed on from a TOTE. As we shall see, locating the conditions for a stop and then executing one, whether by inhibiting action or passing control to a new action, are important features of the brain’s control system.

Fourth, TOTEs can be embedded into one another, yielding a hierarchical organization for control. To illustrate, in our example TOTE, the operational stage “Eat Banana” itself can be decomposed into a hierarchical set of sub-TOTEs for biting, chewing, and swallowing the banana that can control flow among each other to consume the banana.

Figure 1.2C depicts an example of a banana-eating TOTE elaborated in this way. Of course, we can further specify even these sub-TOTE operator steps as sub-sub-TOTEs. For example, the Chew operator in Figure 1.2C can be specified as a flow between two subordinate jaw-control TOTEs, the first of which opens a jaw that tests closed, and a second that closes a jaw that tests open.

This capacity for hierarchical structure means that we can continually specify the control flow at finer and finer levels of detail by making the operator phase of each TOTE another subordinate TOTE, which itself can have additional subordinate TOTEs embedded in its operation.

The hierarchical structure of TOTEs matches the hierarchical structure of action itself. Any given task can be described at multiple levels of abstraction. For example, my morning coffee-making routine has this rough sequence: fill the grinder with beans, turn on the grinder, fill the carafe with water, pour water into the reservoir, put the grounds in the filter, turn on the drip machine, wait. Each of these can be further broken down. For example, putting the grounds in the filter involves getting a coffee filter from the cabinet, unfolding it, filling it from the grinder, and so forth. Each of these sub-sub-tasks can itself be decomposed into sub-sub-sub-tasks until I ultimately end up at a very specific sequence of movements. Thus, the ability to control actions hierarchically is a necessary feature of any control system.

Finally, hierarchical control structures like TOTEs illustrate that we require higher degrees of monitoring the more finely we specify our plan. This is evident even in our simple banana example. Just count up the number of Tests versus Operators in the elaboration of our banana-eating TOTE between Figures 1.2b and 1.2c. As our TOTE tree gets deeper, we add tests at a faster rate than we add operations. This is because whenever we embed, we add a new test but replace an operation one-to-one. Thus, the deeper we plan an action, the greater the demands we place on managing, tracking, and executing all those tests. As we shall see, this is analogous to our cognitive control system, too. Much about cognitive control concerns setting up the right test conditions to govern what operations to engage and when.

The Problem of Cognitive Control

Miller, Galanter, and Pribram’s TOTE architecture provided a first hypothetical answer to the question of what lies between knowledge and action: a control structure, a plan capable of translating a fundamentally nonhierarchical and fuzzy concept of a task or goal into a form that can guide the rigid, precisely timed, and biophysically constrained hierarchy of the motor system.

As a theory of human cognitive control, however, production systems like TOTE oversimplify the problem in significant ways. For instance, there is no place for motivation or mental effort in these test and operator loops. For our TOTE, bananas are eaten. Why one is eating that banana, or how desperately one wants to do so, or, indeed, at what cost is not a part of the structure.

Further, though the computer metaphor has been enormously useful for recognizing the value in control processes and what they might look like at a functional level, this metaphor also has limits. The mind is not a computer program, and the brain is unlike a digital computer. It is not established that the human brain or mind features the kind of context-free computation that enables the versatility of modern computer programming languages to do virtually any task given the right instructions. Thus, before we set out trying to understand cognitive control in this book, it is important to elaborate the problem of cognitive control a bit more and the unique challenges it presents.

Among the reasons that early schools of psychology rejected the notion of a cognitive control faculty was that doing so seemed a theoretical shortcut, the so-called homunculus problem. The worry was that positing control processes that planned and carried out actions amounted to accounting for how we do things in terms of a little person in the head, a homunculus, who knows how to act given the information available and who always takes the right actions to do so. We are left needing to explain what is happening in that little person’s head, and so we are forced to posit another, even littler, person in the first little person’s head, and so on ad infinitum.

Ideas like TOTE and the computer metaphor helped break this infinite regress. In his landmark first textbook on cognitive psychology in 1967, Ulrich Neisser cited the basic homunculus problem and noted that until the advent of the programmable computer, the only clear example of an executive computational agent we had was the human being.[18] But a computer demonstrated that it was possible for a machine to control itself, given the right productions, without need for a homunculus. So, models like TOTE started to chip away at the homunculus problem by showing that one could build an executive agent without resorting to magical little people in the head. However, these models did not fully banish the homunculus.

Take our case of the TOTE agent eating a banana. This model does not require a little person pulling various levers and buttons in our head to coordinate jaw and banana successfully. The control policies it specifies in its TOTE loops and subloops make clear how test-operate cycles could program the behavior without any guiding agent pulling the strings. But how does this TOTE get set up in the first place? How did this system know to test the banana and the jaw in just the way it did? Indeed, if one flipped the Test Jaw Open, Test Jaw Closed operational phases, one could end up with either lockjaw or a serious drooling problem.

Of course, the answer to how that TOTE got set up that particular way is that I wrote it that way. Whoops. We have posited a homunculus, again. But now it is a little programmer, who can figure out what to do and furiously builds the right productions and control policies into our heads as we encounter new situations. That means we’re now back where we started.

To banish the homunculus entirely, then, our theory requires a mechanism not only of control but also of learning. A theory of cognitive control requires a plausible way in which the policies and plans we use are gained through experience, retrieved in the right situation, and elaborated for use by the cognitive control system in a specific situation. Relatedly, that theory of learning should say something about generalization, as in the problem of learning in one specific setting but applying what we learn to multiple other, different settings.

A second fundamental challenge a theory of cognitive control must address is the scaling problem. Again, a central lesson from the study of frontal patients is that cognitive control is concerned with operating in the real world, and often patients’ problems become most apparent only in the open-ended complexity of everyday life, rather than in the simple tasks that we test in the laboratory. Yet, scientists working out theories of cognitive control, including me, have spent the majority of our efforts studying these very tasks. For example, more than 3000 papers have been published on the classic Stroop effect alone. The Stroop effect refers to the observation that it is difficult to identify the font color of a word when that word names a different color (for example: white). Performing this task undoubtedly requires cognitive control to respond to the font color in the face of interference from our automatic tendency to read the word. Yet, this task is poor at predicting a frontal patient’s cognitive control problems in life.[19] Patients show Stroop interference just like everyone else, but whether they show more or less interference on the Stroop task does not consistently predict whether they are likely to get their errands done or successfully hold down a job.

Now, the scientific community is not entirely misguided in our focus on these simple tasks. Rather, we are pursuing the classic scientific strategy of reductionism. Tasks like Stroop are simplified examples—experimental models of cognitive control—that are easily defined and controlled in the laboratory. And because of their simplicity, they also more readily lend themselves to theory. The problem is that we often don’t follow through on this reductionist program. We build elegant models of these simple cases, but we rarely attempt to scale these models up to see if they can explain something about the person’s behavior in the real world. Indeed, many theories of the simple tasks just can’t be scaled. Once the problem becomes complex, the theory that was able to explain the simple problem finds the new problem intractable. Why don’t these theories scale easily to the real world? There are many reasons, but we will discuss three big ones: the curse of dimensionality, the degrees of freedom problem, and the temporal abstraction problem.

The curse of dimensionality refers to the fact that the world around us has many features or “dimensions” that might be important for our actions. Take a look around your room and consider all the features that are there. There are likely many objects and lots of different colors, textures, forms, shading, sounds, smells, and so forth. If you were an alien with no particular advance knowledge of the way things work on our planet, which of these features would be most relevant to your behavior right now? What part of the door is important to get the thing open? What if an off-white wall means something important about the way you should walk? The combinations would be limitless.

Any model of learning cognitive control must at some point address this issue, because it is clear that we humans don’t need to explicitly learn about every feature in our world and its relevance as a test condition for every possible action. We don’t think, “I learned today that the direction of the nap on my rug has no relevance for how I part my hair.” Testing every possible case would take many human lifetimes, let alone the relatively few years of human development. The brain must have a way of culling the options and distilling which information in the world is crucial for our behavior.

Contributing to the scaling problem at the other end of the perception-to-action continuum is the so-called degrees of freedom problem. Basically, this is a fancy way of saying that there are many ways to get the same thing done. So, how do we choose the particular one that we end up executing?

This problem was first recognized in basic motor control, referring to the fact that there are actually many hypothetical ways your arm could configure itself to solve a simple movement problem, like sliding a mug across the counter. However, the degrees of freedom problem becomes more complex, less constrained, and less well defined as we reach more abstract levels in the hierarchy of action and focus on more abstract task goals. For example, there are many, many ways to make a cup of coffee. Some choices probably don’t matter much to the ultimate goal, while others matter greatly. Turning on the drip machine before adding grounds will probably be a messy decision. But should you grind the beans before filling the carafe? It probably doesn’t matter to the ultimate outcome, but the brain still has to choose just one from many ways to do any given thing. Thus, a theory of cognitive control will need to explain how certain plans are specified and actions are taken in a particular situation, even when there are many ways one could act.

Finally, a scalable theory of cognitive control must be able to explain how we generalize what we are doing over time, an ability called temporal abstraction. Many of the simple tasks we conduct in the lab involve a series of short episodes, each of which includes a decision and behavior in response to a particular stimulus. In the experimenter’s argot, these episodes are called trials. Trials are typically unrelated to one another, in that how you respond on one trial does not affect what happens on the next. As a consequence, we can randomly arrange these trials in any order.

The ability to rearrange trials is a valuable feature for designing scientific experiments, but it is pretty unlike tasks in everyday life. Outside the laboratory, episodes are not randomly tossed at us without cause as we go through our day. We don’t lurch from one decision to the next, as if some die roll of fate decides whether we will now eat lunch or take a shower. Rather, our lives and the tasks we do unfold meaningfully in time, over a course of minutes or even hours, rather than seconds. Thus, to control action effectively, the brain’s cognitive control system must both leverage this structure and also maintain a sense of continuity, even when the world itself does not signal what task to do. Further, tasks are often open ended, without a clear plan or path to a well-set endpoint. We embark on many tasks with a general idea of what we want to do and then deal with the specifics on the fly. Thus, the control processes that manage temporal abstraction contribute to the very continuity and flow of our lives, and they are at the basis of our ability to be effective, goal-driven people.

The problem of cognitive control is one of bridging the gap from knowledge to action in a complex world. With this problem of cognitive control more clearly before us, in the remainder of the book we will consider the mechanisms by which the brain might solve it. Cognitive and brain sciences in the last several years have provided us with a number of important clues to these mechanisms. We will discuss these discoveries with an eye to both the power and the limits of current evidence and theory.

In the first part of the book, we will lay the theoretical foundation for understanding cognitive control function. We will first consider the evolutionary origins of cognitive control in the mind and brain, with a focus on the emergence in our ancestors of a capacity for detailed, hypothetical future thinking and compositional action planning. Then, we will delve under the hood and take a close look at the nuts and bolts of cognitive control. We will introduce the cognitive and neural mechanisms at the basis of cognitive control function, and we will then consider how they help us to resist impulses, avoid errors, and choose the correct courses of action. We will also see how the brain has elaborated these basic mechanisms to handle complex tasks that are structured hierarchically and change over time and place.

Equipped with this theoretical background, in the latter half of the book we will consider the many facets of cognitive control in our everyday functioning. We will see not only that we are bad at multitasking but why we are bad at it. We will consider the problem of inhibition, or stopping ourselves from doing unwanted actions or thinking unwanted thoughts. We will explore the close relationship of motivation and cognitive control and will see how control systems not only help us achieve the ends we want but also balance them against the means we don’t like. We will see how control makes our memories work for us. And, finally, we will consider how the mechanisms of control change over our lifespan, in childhood and through adulthood to old age.

Along the way, we will gain insight into the reasons we do the kinds of things we do. We will learn about ways that our control systems fail and ways that we might be able to make them better. We will introduce some concepts about the human mind and brain that might surprise you while also dispelling some other widely held, but incorrect, beliefs. Overall, we will build an overarching account of how we link thought and action in our everyday lives.

Finally, I feel obliged to caution the reader that these are challenging goals. There is presently no accepted, unified theory of cognitive control that addresses all the challenges we have discussed so far. This includes the theoretical ideas that will frame the topics in the rest of this book. To be frank, it is very likely that you can find someone who disagrees with just about everything I write in the following pages. Some ideas still await enough data to be fully supported, while others have seen conflicting evidence. Don’t despair, however; this uncertainty and debate are the norm for a healthy science. And it is worthwhile taking this hard problem on in a unified way, despite its challenges, because the mystery of cognitive control, of that cup of coffee, is important to solve. As we have seen, cognitive control influences the very flow and enterprise of our lives. So, while trying to understand it better we might also learn something about ourselves along the way.

CHAPTER 2

The Origins of Human Cognitive Control

Around half a million years ago, a hairy, lanky bipedal creature knelt in what is now a gravel quarry in the parish of Boxgrove in West Sussex, England. It held a hammering stone in its left hand, and in its right, a rough-hewn flint hand axe that it had brought there to sharpen and refine. In a single sitting, this ancestor of Homo sapiens, named Homo heidelbergensis, knelt and knapped away at its axe with the hammer, first probing and tapping, and then with a focused strike, knocking off larger flakes of stone. The axe it was crafting is called a biface by archaeologists because it would have had two symmetrical faces, angled to a point at one end, with the other blunt and easy to wield in the hand. As a tool, the axe would have been akin to a Middle Pleistocene Swiss Army knife. It could cut, stab, grind, scrape, or pound depending on the task, and so these axes were likely central to life for early hominids.

Crafting the axe itself would have been an exacting process. As our Boxgrove knapper worked, bits of flint flaked off with each hammer strike, piling up along the inside of the creature’s kneeling thighs and scattering out in a fanned pattern before it. Occasionally, a bigger or uniquely shaped piece would flake off. Noticing this, the knapper would pause its work, pick up this piece, and place it in a nearby pile to sort through later. These bigger pieces of flint could be useful for all sorts of other purposes, and so going through this pile might yield some valuable additional tools to take along with the sharpened axe. Once it finished with its tasks, the knapper went on its way, toting its new tools with it and leaving behind only the dust and debris of its labor.

This sequence of events by a single Homo heidelbergensis on one day some 500,000 years ago was reconstructed in detail, down to the hand in which it held its hammer, by archaeologists Mark Roberts and Simon Parfitt.[1] They were able to do so because the scatter of flint dust and flakes the Homo heidelbergensis left behind was stunningly well preserved and uncontaminated by other animal or hominid activity. In these archaeologists’ hands, the flakes were the evidence of a sequential task, a plan that unfolded in time to achieve a goal.

It is tantalizing to imagine we see in this ancient ancestor’s industry the echoes of our modern capacity to get things done. The task certainly holds some basic characteristics of complex goal-directed behavior. The goal of a refined hand axe can be conceived of in advance and pursued over multiple episodes and settings. Indeed, the axe was not initially crafted at the Boxgrove site but was brought there from another place just for refining. The goal of having a refined hand axe was abstract enough to have persisted over these changes in time and place.

The process of making the axe itself was sequential and required managing many subtasks and subgoals. The biface is an example of what archeologists term Acheulean stone industry, an advanced form of flint knapping that is distinguished partly from the earlier simpler forms of stone toolmaking, termed Oldowan, by its complex, multistep crafting process (Figure 2.1). The coordination, planning, monitoring, and skill required to manufacture this advanced type of hand axe is not seen in any other species, even those that use tools. And, for humans at least, Acheulean axe crafting engages cognitive control systems. Neuroimaging experiments by anthropologist Shelby Putt have shown that when modern humans flint knap to make an Acheulean axe, in contrast with simpler Oldowan forms, they exhibit more activity in the same frontal and parietal brain networks associated with cognitive control function.[2]

That pile of bigger flakes that our Boxgrove knapper set aside to go through later is perhaps even more interesting than the axe. Cognitive archeologists Thomas Wynn and Frederick Coolidge have speculated that this second pile is evidence of early multitasking.[3] In this interpretation, this Homo heidelbergensis had at least two goals: one was to refine its axe, and the second was to find some other useful flakes along the way. While knapping its axe, it monitored for a nice big flake. When one came along it paused one task and switched to the second one to place the flake in that separate pile. And, of course, the separate pile itself implies an intention to go through that pile later to find the most useful flakes. Under this interpretation, then, the Homo heidelbergensis had both foresight and the ability to work toward at least two goals at once.

FIGURE 2.1. Examples of ancient stone industry. The Acheulean hand axe from the Early to Middle Pleistocene pictured on the left is marked by its biface blade on one side and exhibits a higher degree of sophistication and skill than the earlier Oldowan form pictured on the right.

How do we draw a line from the Homo heidelbergensis, who was smart enough to have engaged in this day of axe refinement, to modern humans, who are smart enough to reconstruct that day based only on a preserved pattern of flake scatter? This is an important question to answer, because it provides a functional context for any ideas we might have about cognitive control and the mechanisms by which the modern human brain achieves it.

It is by now a cliché to state that nothing in biology makes sense except in the light of evolution. Nevertheless, the insight in this maxim should not be missed. By considering how and why humans’ capacity for cognitive control emerged, we can frame how we think about cognitive control function. What are its goals? What are its adaptive advantages? What are its costs? Our answers to these questions provide an important framing for the control mechanisms we will consider in later chapters. In this chapter, we will consider why and how human control function evolved, even given the scarcity of evidence and its known limitations.

Compositionality and Generativity of Human Behavior

What changes in cognitive and brain function mark the evolution of cognitive control? Tracing the evolutionary history of a cognitive function is a hard task. If you are interested in the evolution of a wing or a claw, you can place fossils next to one another chronologically and have a pretty concrete picture of how the structure changed over generations. But you can’t excavate a thought or dust off fragments of a goal. Rather, archaeologists must rely on the traces that our ancestors left behind and try to reason about the behaviors and cognitive processes that would have been required to produce them.

Of course, this approach has many limitations that are well known to archaeologists and anthropologists. For example, the mere construction of a sophisticated item, like an Acheulean hand axe, is not sufficient evidence of high intelligence or advanced cognitive control function. Many species include in their behavioral repertoire the construction of complicated, intricate things without needing anything like an advanced cognitive control system.

Take the example of a spider’s web. Spider webs are remarkably sophisticated and effective tools for predation.[4] Across species, some use 2D and others use 3D web designs. Some webs are stationary air filters, while others are nets that move with the air. A single web incorporates different silks, ranging from strong structural threads to soft and sticky capture threads. The construction of the familiar “orb web” involves a sophisticated sequence of behaviors to construct a hub, frame, and radii. Spiders build with seeming purpose, taking shortcuts, adding scaffolding strands, and then removing them when done. They correct and revise as they go. Individual spiders can even alter the mesh density and structure of their web designs to accommodate differences in vegetation, climate, and the type of prey in the area.

If Homo heidelbergensis had made primate-sized spider webs, one could imagine the gushing words written on the topic: surely these prehistoric primate webs are evidence of a mental plan and of behavior guided by the image of a future web. Only an advanced memory could have stored all the necessary steps to implement such a plan. Managing the multiple tasks of frame, hub, and spiral building all while monitoring the overall web design surely required cognitive control. And language must have followed from the need to discuss the latest web designs with friends and boast of that juicy bug that still lingered at the lips.

Of course, no one is claiming the spider possesses these advanced cognitive abilities. Indeed, spider web building is a great example of an exquisitely complex behavior that can be achieved with only a few simple rules. For example, scientists can simulate orb spider web building, including adaptation to environmental variation, using a virtual spider that makes decisions based only on the most recent threads it has touched.[5] Thus, the spider has no need for a deep planning capacity, cognitive control, or a goal of the web it wants to build. Four hundred million years of evolution have endowed the spider with a set of behavioral instructions mapped to its particular sensory apparatus. Following these rules is sufficient to produce a complex and adaptable web that meets the spider’s needs.

It is not possible, then, to look at a particular behavior or the product of a behavior, like an axe or a web, and infer with certainty what cognitive processes are required to do that thing. Just because humans complete a task using cognitive control, does not mean another species is also using cognitive control for that task. One certainly has more confidence that a recent ancestor of a human would perform a task in a way similar to the way a human would, and would use a similar brain system. But, strictly speaking, it remains an inferential leap to assume that because modern humans use cognitive control systems to make Acheulean hand axes, ancestors of humans must have done so, too. If I scanned the brains of humans while they somehow tried to construct spider webs, it would surprise no one that they would show activity in frontal and parietal areas related to cognitive control. Put plainly, given our particular neural and cognitive specialization, we would lean on our cognitive control systems for a difficult and unfamiliar problem like web making. But such an observation says next to nothing about the spider.

To interpret the archaeological record, then, we need to have a firm idea of the kinds of functions we think evolved. What is particularly characteristic of human cognitive control that we might look for in the industry of our ancestors? As a good starting place, it should not be overlooked that a human being could actually plan and construct a spider web, given the right materials, tools, and desire to do so. Even if it had opposable thumbs, a spider would never be able make a stone axe. Indeed, it would never occur to the spider that a future existed in which it was the owner of such an item.

Herein lies the basic asymmetry. Our human cognitive control system is general and has two basic ingredients. First, we are capable of conceiving of future situations and goals we have never experienced or considered before, within either our own lifetime or the lifetimes of our ancestors. Second, our control systems can plot out the complex actions necessary to achieve that future. We can readily bridge thought with action. Other species for the most part have to discover an action through trial and error or wait on evolution to give them the solution. So, unlike the spider that never even had a goal of a spider web, the human could make a web but would likely have a goal to do so and then plan the way to the final product using cognitive control.

This distinction is evident in the limits on current artificial intelligence (AI) and is why anxiety over the coming AI dystopia is misplaced. The recent achievements of AI are remarkable. Computers are driving cars, beating humans at chess and Go, and recognizing speech. Machines are doing things they weren’t doing even five years ago, and they are often doing them better than humans.

These are certainly impressive victories for AI, and it is easy to see why many people, like the CEO of Tesla and SpaceX, Elon Musk, reason that this is the beginning of inexorable evolution toward an artificial superintelligence that controls our fates, rather than the other way around. At some point, we will surely build an AI that is able to build better AIs than we do, which can, in turn, build even better AI-building-AIs. At that point, the rest is mere inevitability.

However, the panic is premature. We are still a long way from building a generalist intelligence[6] akin to that of humans. Rather, we are getting better at building specialist intelligences more akin to that of the spider. As one example, AIs can now be built to master Atari games like the 1976 game Breakout. In this game, the player controls a horizontal rectangle, called the paddle, positioned at the bottom of the screen. The paddle is used to deflect a ball to bounce up and hit a set of colored blocks at the top of the screen, removing them. The objective is to remove all the blocks without missing the ball when it is deflected back down. AI systems built using large neural networks not only can master this game, they can achieve scores superior to those of human players. However, this does not mean that we are on threshold of being ruled by The Computer in Alpha Complex. Why not?

First, this Breakout-playing program is highly intelligent about Breakout, but that is the limit of its intelligence, which is fully specialized to performing that task. A human is not only able learn and play Breakout but will finish playing Breakout, grab a coffee, fire off a tweet about being beaten by a computer, and then head home to make dinner. No AI can do that.

Moreover, our Breakout-specialist AI is likely even more specialized than you might think. Many AIs are a specialist not just in Breakout but in that exact version of Breakout. They will struggle to sustain their performance when even simple changes are made to the game they know.[7] For example, imagine you move the paddle bar up by several hundred pixels, offsetting it from its standard position. A human might be thrown off for a moment, if at all. If that had never happened before in the AI’s experience, it would likely regress almost to the beginning of its training.

Why aren’t humans devastated by these changes, as the AI is? One answer is our ability to think about tasks in terms of their constituent parts, like the different branches in the task hierarchies we discussed in chapter 1. In other words, we think, plan, and reason about tasks compositionally. As a result, we are able to modify parts of tasks in response to changes without affecting what we know about the rest of the task. We can adjust to a new paddle position without losing what we know about ball physics or the objective of the game.

Compositionality also lets us take useful subparts of old tasks with us to new tasks as the need arises without having to relearn the whole task. For example, a human player who mastered Breakout would likely be quick to master the game Pong, as well. In Pong, the paddle bar is vertical rather than horizontal, but the basic control is the same. The paddle is used to reflect a ball across the screen past an opponent’s paddle. Humans would be able to take their understanding of paddle control and the physics of the square ball bouncing from Breakout and apply it almost immediately to Pong.

Psychologists call this ability transfer. In this case, the transfer is positive because something learned in one context is applicable in a new context in a beneficial way. Moreover, here we are taking only one component of that previous task, the paddle control, to the new task. If we didn’t think compositionally, we might end up with a different kind of transfer, called negative transfer. For example, if we also took the objective from Breakout to Pong, namely, trying to hit the rectangle across from us, we would be worse at Pong than if we’d never played Breakout at all!

Control makes our task performance generative, meaning that we can not only conceive of but also carry out tasks that we have never done before and for which evolution has not endowed us with a hard-wired program. Control achieves its generativity by operating over a compositional action structure, combining and recombining precompiled parts of tasks we have performed before to carry out the new tasks.

Notably, these qualities of generativity and compositionality are also hallmarks of another uniquely human adaptation: language. This similarity is probably not coincidental, as language and cognitive control share a close relationship, and the adaptive success of each probably relied in some part on the status of the other. Indeed, there is a long-standing debate as to whether they are fully separate systems.

Episodic Future Thought

Importantly, compositional action is only half the story. We pair our generative and compositional action system with an ability to envision new, fantastic, counterfactual, and hypothetical scenarios. Cognitive neuroscientists call this ability episodic future thought.[8]

Imagine for a moment what your birthday will be like next year. Try to make your image as vivid as possible. Picture the scene and those acting in it. Walk through the events in time. What do you say to people? How do they respond? Is there food? What time of day is it? Now, think of something funny or even scandalous that happens during the party. Imagine how everyone reacts and what happens to the party from there.

How detailed did your image of this future birthday get? If you are like most people, then this hypothetical future was rich and detailed, indeed. It is an amazing facet of your cognition that you managed to generate this detailed image of a precise event that has never actually happened.

We humans have a profound ability to conceive of hypothetical futures, whether immediate or distant. We can even consider futures that could never happen. We can imagine ourselves as a colonist on Mars or as a fish swimming around a pond full of frogs or as a toaster oven. Even with these impossibilities, we can generate a pretty detailed image. Further, we can plan for the future at varying degrees of granularity. We can run little simulations in our head, walking through hypothetical events at an exquisite level of detail. Or if we want, we can also predict coarser future states or even guess at likely outcomes without much detail at all. For example, predicting that we’ll enjoy lunch tomorrow with a friend doesn’t require simulating the moment-to-moment events of the episode. How do we do this?

The first clue is that this type of detailed future thinking has a close relationship to our ability to remember our personal past, termed episodic memory.[9] Episodic memory specifically refers to our ability to remember episodes or experiences from a particular time and place, and typically features rich detail. Further, these remembered episodes come along with a conscious re-experiencing—an intimate, personal feeling of belonging in this past. In other words, with an episodic memory, you feel you are there and you know it to be your own experience rather than someone else’s.

In the brain, episodic memory partly relies on the hippocampus, a structure found deep inside each temporal lobe.[10] If the hippocampus is damaged, a person can develop amnesia, which is a disorder of episodic memory. Now, the term amnesia can be confusing. Real amnesia bears little resemblance to the amnesia familiar from such blockbuster thrillers as The Bourne Identity. Real amnesics, like the famous patient HM, lose memories of the past.[11] But, unlike in movie amnesia, it is their more recent memories that are vulnerable to loss, not their old ones, like their name or their occupation. Further, real amnesics find themselves unable to form new episodic memories. As soon as a memory is no longer held in the immediacy of consciousness, it may be lost entirely.

Importantly, however, our memories are not just mere snapshots of a particular time and place. For our memories to be meaningful and useful, we place episodes within a narrative that fits with our general knowledge of the world. Thus, we don’t remember our past just by opening some file drawer in our brain and pulling out whatever is in there. Rather, we piece together individual recollections along with a lot of inference to construct a model of a likely past. Scientists sometimes call these models a schema.[12]

To see why schemas are powerful for remembering, imagine that you have a box of old wedding photos. Each photo is like an individual memory. It is a moment of knowledge that captures an arrangement of people and objects at a particular place and time when the snapshot was clicked. You can think of the hippocampus as being essential for taking these snapshots and pulling them back up to consciousness on cue. However, as a disorganized pile in a box, the individual photos cannot provide much beyond what each captured at a given moment.

Now, imagine that you construct an album from those photos, organizing them in time or by a theme. For example, in one picture Uncle Ziad is happily dancing the Macarena. In a later picture, he is seen at a table holding an ice pack on his knee. We can guess what befell poor Uncle Ziad, though it is never pictured. A story unfolds in this organization of photos that would be difficult to discern from the individual snapshots. This constructive nature of our memory is what gives it its power.

The knowledge that results from organizing our memories is more than the sum total of the individual pictures. There is a story in the gaps. In this form, a schema, like a photo album, can be generative. Because you have general knowledge about the world, you can draw inferences about things that must have happened that are not present in the individual memories.

In the brain, schemas depend on a broad network of areas that include the hippocampus and the ventromedial prefrontal cortex (VMPFC). Evidence from neuroimaging experiments using functional magnetic resonance imaging (fMRI) suggests that VMPFC interacts with the hippocampus and other brain regions when people use schemas to draw inferences and make decisions based on their memories.[13] Likewise, patients with damage to the VMPFC do not show dense amnesias, but they do exhibit problems using schemas.[14]

As one example, VMPFC patients often confabulate.[15] Confabulations are distortions of memory in which the patient remembers events that never happened and that are incongruous with reality. A confabulating patient might explain that they were delayed arriving for an appointment with their doctor because they could not find a place to park. They will then seem unconcerned when it is pointed out to them that they neither drive nor own a car. Or they might complain of the hassle caused by a broken arm, all the while clearly having no cast or injury of any kind. Though confabulations are often quite fantastic, they are also not intentional attempts to lie or deceive. Rather, they are a wrong or even bizarre reconstruction of a past or present reality. Owing to their brain damage, these patients are in some way failing to evaluate their memories within a plausible model of the world.

Confabulating patients offer an extreme case of a more general facet of memory that we all experience: false memories.[16] False memories can cause us to place someone at an event they never attended or to remember hearing a fact we never actually heard. Indeed, in rare cases, false memories can be the source of considerable pain and injustice. One telling statistic from the nonprofit Innocence Project[17] is the estimate that close to three quarters of false convictions overturned by DNA evidence have been a result of witness misidentification—in other words, a false memory of who committed a crime.

Why would we evolve to have a memory system so vulnerable to distortion, given the obvious costs? In some ways, false memories are worse than forgetting: at least when we forget, we don’t remember anything. Remembering something that is wrong can lead to real problems.

One answer to this question is that our memory system did not evolve to accurately reconstruct the past. Rather, the hippocampus, VMPFC, and other components of the memory system in the brain evolved for schema building. Schemas are useful not just for reconstructing likely pasts; they are also crucial for forming ideas about possible futures. Our explicit memories are there to inform our futures, to draw on events of the past to model a plausible future. This ability to model the future is the adaptive feature that drove the evolution of our memory systems, rather than the ability to accurately recall our personal pasts. No one failed to reproduce because they forgot the exact number of stripes on that tiger they saw. But they might have if they couldn’t use that one encounter they survived to generate hypothetical models of future encounters with a tiger! False memories are the price we pay for having a generative memory system.[18]

If this view of memory is correct, a key hypothesis is that detailed episodic future thought should rely on the same hippocampal and VMPFC systems in the brain that episodic memory does. Neuroimaging experiments have found evidence consistent with this hypothesis, with the same brain networks consistently being activated whether people remember a past event or envisage a future one.[19] Further, when patients with amnesia are asked to imagine future scenarios, the futures they envision have fewer details and lack the vividness of those of people of a similar age and education who don’t have this memory disorder.[20] Patients with confabulation disorder likewise have difficulty planning their personal futures. For example, one such patient answered questions about what she would do later by saying she was going shopping or cooking supper. Yet, she had never gone shopping or cooked anything since acquiring the disease that led to her disorder.[21]

It is this system of future episodic thought, built on our episodic memory system, that allows us to engage in advanced planning and goal generation. Rich episodic future thinking can leverage our vast stored knowledge of facts and experiences to do things like conceive of making a spider web in the future. It follows that this capacity to generate goals is central to understanding the evolution of the system we use to achieve them, cognitive control. And to some degree, our vast ability to engage in this kind of general, hypothetical, and even counterfactual thinking may be particularly characteristic of our species.

Now, any claim of a unique human capacity will be contentious, and episodic future thought is not an exception. Certainly, there is evidence for episodic memory being used for planning by other species.[22] And in some cases, memory is used for foresight.[23] For example, scrub jays will cache worms, hiding them for future consumption; they remember where they put them; they will not return to ones that have rotted; they will even replant worms in a new location if they are spotted caching them by another jay. However, this case, like most other examples of foresight in nonhuman species, is specific to a domain, like worm caching, that could reflect specific evolved behavioral adaptions rather than a general capacity. Nonetheless, it is certainly reasonable that episodic future thinking is found in other species and similarly relies on episodic memory systems.

But even if some capacity for future thought exists in other species, none plans for the future on the scale, depth, and generality that humans do. Human societies have contingency plans in place for asteroid strikes and hunger crises that may never come. Human children enjoy filling a box with various buttons and notes, and then burying it in hopes that some hypothetical future human, maybe they themselves in 50 years, might get a thrill. The day a scrub jay plants a time capsule instead of a worm, we might need to seriously reconsider matters.

In general, then, the comparison is not close. Humans have an unrivaled and remarkable capacity to conceive of vivid future scenarios and possibilities. Nevertheless, as scientists, we must define concrete ways to identify when a species that can’t talk to us is or is not engaging in episodic future thought.

Psychologist Thomas Suddendorf has focused on episodic future thought as a capacity that might separate humans from other species.[24] He proposes a reasonable set of minimal conditions that should be met to test for the presence of episodic future thought in any species.[25] First, the animal should be able to solve a problem in only a single attempt without repetition or trial and error. Second, the animal should solve a problem in a new context that it (and its ancestors) hasn’t previously encountered. This rules out niche specializations, like the scrub jays and their worm-caching behavior. For similar reasons, multiple tests should occur in different contexts, in both space and time. For example, planning should be for a goal occurring perhaps a few seconds, a few minutes, and a few hours ahead, or even the next day.

Apart from some interesting anecdotal reports in apes, I know of no nonhuman animal that been shown to meet all of Suddendorf’s criteria. In contrast, episodic future thinking in humans that meets Suddendorf’s criteria appears to emerge by around 4 years of age.

For example, in one experiment, Suddendorf and Jonathan Redshaw showed children an upside-down Y-shaped tube (with the single hole at the top and two holes at the bottom) placed within reach of the child’s hands.[26] They allowed children to watch a toy being dropped from the top of the tube and exiting one of the bottom holes for a few trials. Then, the children were given a chance to get the toy if they could catch it coming out the bottom of the tube.

Two-year old children consistently placed their hand under only one of the openings. Consequently, they caught the toy about half the time. Four-year-olds, however, immediately placed their hands over both tube ends from the first attempt, anticipating that the toy had to come out one of the holes and guaranteeing they would get the toy.

Chimps and orangutans placed in the same setup, though with a desirable food rather than a toy, behaved just as the two-year-old children did. None placed their hands over both tube ends on the first trial. Most never figured out to do it over multiple trials. Even the one animal that did use two hands for a while eventually went back to placing one hand rather than two.

There is other evidence that the capacity for future episodic thinking emerges by early childhood in humans. For example, children in the 4–5 age range can adjust their behavior based on the reliability of future outcomes.[27] They are able to forecast their own future emotions,[28] and they show the beginnings of social reasoning, such as being able to take the perspective of others.[29] They engage in pretend play, often involving worlds or situations that don’t exist.[30] If your children are running around the house pretending to be airplanes, this is not because they are instinctually practicing for when they will be airplanes as adults. If there is a muscle they are working for later life, it is the ability for episodic future thinking.

Another capacity that Suddendorf highlights in four- and five-year-olds that is particularly relevant to cognitive control is the capacity for deliberate practice.[31] Deliberate practice is interesting because it has the primary goal of improving our hypothetical future selves. Indeed, practice does not require an immediate positive outcome at all; it can be entirely motivated by this hypothetical future.

Consider that a chimp might repeatedly plunge a stick down an anthill and so would get more and more skilled at fishing for ants. But this would not count as deliberate practice, because it is the tasty ants that are motivating the chimp’s practice, not the image of its future self as a famous, world-class ant fisherman. However, children might repeatedly practice dribbling a soccer ball around a cone, not because doing so is loads of fun but, rather, because they have an image of their future selves as Lionel Messi. By four years of age, children will engage in practice of a skill that might be useful for a future reward. By seven, children understand the advantages of deliberate practice and will engage in it unprompted if motivated by the ultimate goal.[32]

Further, human practice is often directed to the components of a skill, rather than the full skill itself. The pianist will practice only a few challenging measures of an entire piece to perfection. The tennis player will repeatedly practice the throw that precedes service delivery. A dancer will do one step sequence again and again until it is effortless. This ability to practice parts of skills reflects the compositional structure of our action representations. We carve up skills into subskills, and we can think in these terms to organize our practice. Thus, there is a tie between our ability to imagine future scenarios and the compositional nature of our action representation, and the two interact with one another to build skills and master tasks. This pairing would have been a powerful combination indeed for our ancestors, as it is for us.

When Did Human Cognitive Control Emerge?

So far, we have emphasized two capacities in the evolution of human task performance: a capacity for rich, episodic future thought paired with a capacity for compositional action control. When did evidence of these two capacities arise among the ancestors of modern humans?

The emergence of modern human intelligence remains a point of considerable contention among scholars. The earliest evidence of anatomically modern humans has been dated to 150,000 to 200,000 years ago.[33] However, early hominids exhibited increased cranial capacity and advanced toolmaking as far back as over a million years ago.[34] Some scientists argue there was a grade shift whereby our modern cognitive capacities emerged rapidly and recently, as a massive upgrade from a primitive apelike intelligence in even our recent ancestors.[35] Others argue that the process was more gradual, with sophisticated capacities emerging in earlier ancestors that coalesced into our current cognitive capacity, perhaps through cultural transmission.[36]

Regardless of when and how our cognitive abilities emerged, however, most scholars agree that they were in place by 50,000 to 80,000 years ago. This Upper Paleolithic period was marked by an expansion in the variety and variability of tools and tool materials.[37] Not only were lots of new tools being built to solve new problems, but also many different types of tools were emerging for solving the same problem. This rich variability contrasted sharply with the kind of toolmaking that had prevailed during earlier periods. For example, earlier species of Homo, like Homo erectus, constructed effective stone tools, such as the bifacial Acheulean hand axes, with a level of sophistication unseen in other primates. Nevertheless, this tool design and its basic method of construction were maintained with only moderate changes for around 1 million years. It was not until the Upper Paleolithic that innovation on this basic design really took off, and then it changed almost constantly. Thus, H. sapiens was distinguished from its predecessors not just by building sophisticated or componential tools but by doing so with such apparent ingenuity.

H. sapiens populations also greatly expanded the environments in which they lived during this period and, accordingly, rapidly adapted their diet, customs, and patterns of life to match these new circumstances. They settled successfully throughout Europe, Asia, and Australia, interbreeding with the local Homo species, including the Neanderthal. They developed new types of foraging and hunting behaviors, including fishing, land management, and herding. These adaptations were rapid, finding solutions to new challenges far more quickly than the generational plod of natural selection. Each adaptation led to further migrations and innovation.

For example, changes in the shape and strength of bones from fossils of H. sapiens feet as far back as 40,000 years ago in China provide evidence that protective footwear was used habitually.[38] The development of protective footwear would have permitted migration into colder environments, including a difficult migration through the colds of northern Siberia that ultimately led to the colonization of North America, likely by 15,000 to 20,000 years ago.

Thus, over the course of about 40,000 years, H. sapiens had gone from living in small pockets in Africa and Asia to surviving successfully on six of the seven major continents on the planet. Although 40,000 years is a long time, it is but a blink of an eye in the context of the millions of years of hominid evolution that preceded it.

Rapid success in these variable environments was no doubt made possible by H. sapiens’s general cognitive capacity. For example, the H. sapiens who migrated to colder environments were able to use animal skins and furs and protective footwear to survive and reproduce. In other words, they conceived of a future in which they were wearing a thick coat, something they had never seen before on themselves but might have seen on other animals. With this image in mind, they were able to conduct the tasks and subtasks necessary—from hunting to crafting—to make the coat a reality. And if they survived even one winter, they managed to reach this goal within the span of a year, not multiple generations.

Is there any evidence, beyond the prodigious ingenuity of H. sapiens in this period, that they could conceive of counterfactual or hypothetical futures? To answer this, we might look to the emergence of art and abstract symbolism during this period. The Upper Paleolithic is the first point in the lineage of modern humans when we see unambiguous and common evidence of a capacity for abstract thought in the emergence of art, symbolic artifacts, and apparent religious iconography. The artwork crafted did not always depict a concrete reality or a direct representation of the observed world. Take, for example, the Hohlenstein-Stadel lion man, a carved sculpture, sketched in Figure 2.2, which dates to some 30,000 years ago.[39] The significance of this item for the people who constructed it is unknown, but it is profoundly interesting because it is a representation of a thing that does not exist. Whoever carved this statue had never seen a lion person, was not planning to hunt or eat one, didn’t plan to marry one, and hadn’t been menaced by one. Yet they conceived of a lion person, and they used this conception to plan out a set of actions to carve what they conceived.

Similarly, unlike tools and artifacts with clear uses, the shape and construction of this object was not constrained directly by whatever its carver wanted to use it for. Consider that the shape of an axe is mostly constrained by its purpose, namely, being wieldable and capable of splitting a target material. So, an effective axe handle is long and thin enough to hold but thick enough not to break on impact or flop around. The head of an axe needs to cleave, and so it had best involve some kind of a wedge to focus and amplify the force applied to the broad end. These are constraints that exist outside the mind of the maker. Thus, the axe design could conceivably come about through trial and error until the tool’s desired purpose was achieved. Given enough time, an animal with no plan or design might stumble onto the right shape. In contrast, the Hohlenstein-Stadel lion man and similar artifacts are hard to explain as anything but a pure construction of its carver’s imagination. Little in the world constrains their particular shape.

FIGURE 2.2. Hohlenstein-Stadel lion man statue that was constructed by an H. sapiens some 35,000 to 40,000 years ago. Its purpose and significance are unknown, but it is a representation of an abstract, hypothetical referent.

So, what change underwrote this burst of ingenuity some 60,000 years ago? Was it cognitive control? To some degree, this question is probably ill posed. Our cognitive control capacity, like all mental faculties, evolved through a process of natural selection. Thus, I am deeply skeptical of theories proposing a sudden change, particularly of a complex cognitive function, like cognitive control, that interacts with so many other cognitive systems. Evolution is a gradual process, with each change progressing from the state preceding it, and attempts to locate one process or mental faculty that emerged suddenly in humans generally collapse on inspection.

For example, one influential hypothesis proposed the sudden emergence of an advanced working memory around 60,000 years ago.[40] Working memory refers to our ability to hold on to a small amount of information for a short period of time in an active state so that we can maintain it, manipulate it in our minds, and act on it. Working memory is central to cognitive control, and we will discuss it extensively in the next chapter. But many species of animals, even mice, display some form of working memory. So, whatever the special sauce is that makes human working memory “advanced” needs to be specified. Otherwise, one has simply posited another homunculus, a theoretical shortcut that itself needs to be explained. One is left chasing one’s theoretical tail and has not really explained anything.

A further issue with sudden single-process ideas is that arguably there were progressive enhancements in cognitive control that emerged well before 60,000 years ago. For example, a putative advantage of an advanced working memory is the ability to manage multiple goals at once. This is a key requirement for the kind of compositionality we have highlighted as a feature of our action system. But recall the Boxgrove knapper at the outset of this chapter. This H. heidelbergensis showed evidence of multitasking 500,000 years ago, managing more than one goal at a time while making its axe. Further, though the basic Acheulean axe design underwent slow change relative to the explosion of the Upper Paleolithic, it did undergo changes that look like the progressive development of deeper compositional structure.

Anthropologist Dietrich Stout and colleagues have compared the artifacts of the Middle Pleistocene excavated from Boxgrove with bifaces knapped by experienced and novice human knappers in order to analyze the goal structure that might have contributed to their construction.[41] As illustrated in Figure 2.3, later Acheulean axe construction required a deeper goal hierarchy and more branching among subgoals than the axes constructed during the early Acheulean period. This change over time toward deeper goal hierarchies and richer compositionality appears gradual and progressive rather than sudden. Further, these changes were occurring in the Early to Middle Pleistocene, more than half a million years ago.

If control systems were progressing gradually, perhaps emergence of cognitive control is not responsible for the “great leap forward” of the Upper Pleistocene, and we should be looking elsewhere for a single faculty that drives the cognitive gulf between us and other primates. Indeed, maybe it was episodic future thought, rather than working memory or cognitive control, that emerged suddenly at this late point, evident in the art and symbolic artifacts that arose in this period.

FIGURE 2.3.. Progression of goal hierarchies required for Acheulean stone industry between the early and late Acheulean periods. Adapted from Stout et al. (2014), Figure 11.

As already noted, however, the neural systems known to support episodic future thought are the same as those needed for detailed episodic memory. These systems serve a similar memory function in other mammals, as well. Indeed, mice use replay for some decision making and way-finding tasks.[42] This suggests that the capacity for episodic future thought, though expanded in humans, also has its roots in a highly conserved and ancient system for retrieving past events in the service of informing future behavior. Thus, the progress of episodic future thinking was likely gradual, too, evolving from some vestigial, but advanced, form present in earlier hominids.

In light of these considerations, I favor another possibility I think fits better with both the evidence and a progressive view of evolution. Perhaps the final explosion of innovation seen in the Upper Pleistocene reflects an interaction between multiple gradually expanding cognitive capacities that could maximally leverage cultural transmission. Consider, in particular, the systems for episodic future thought and the system for compositional action control discussed in this chapter. Each system complements the other. Episodic thought allows us to conceive of more sophisticated future goals, whereas our control system can organize that thought process and match it to sophisticated new actions. Though each system might change incrementally on its own, both changing at once would cause an accelerating shift in our capacity to get things done; indeed, the accelerating change might look stepwise.

Another source of data that might be helpful for understanding how cognitive control emerged, then, comes from the brain. Of course, it’s hard to excavate the brains of our ancestors. Most of what we understand about the evolution of the human brain comes from careful anatomical comparison of our brains with those of other species. What anatomical features correlate with greater capacities for cognitive control and intelligence across species? What differentiates primate brains from those of other mammals, ape brains from those of other primates, and of course, those of our nearest ape cousins from ours?

One of the most consistently cited differences among species is overall brain size, and relative to other primates, humans have large brains. It makes intuitive sense that a bigger brain would correlate with higher intelligence: bigger brains presumably contain more stuff, which includes more neurons. More neurons likely mean more connections and so a higher capacity to do more complex computations. And, indeed, there is a rough correspondence between brain size and expressed intelligence across species. Cats have bigger brains than rats, but smaller brains than monkeys, who in turn have smaller brains than apes.

There is a problem with brain size as a metric on its own, however. Animals with bigger brains also have bigger bodies to go with them.[43] Indeed, humans do not have the largest brains in the animal kingdom, not by a long shot. The human brain, which weighs about 3 pounds, is a quarter of the size of an elephant brain, and a sixth the size of a sperm whale brain. As sperm whales aren’t about to outwit anyone short of a squid, overall brain size is likely not the sole determinant of intelligence.

Allometric studies that measure relative brain size while correcting for body size, however, do find that several properties of the human brain are supersized relative to those of other animals, including other primates. Humans have a higher ratio of overall brain weight to body weight than other species.[44] Further, humans have a larger volume of neocortex relative to the rest of the brain than other species.[45]

Neocortex refers to the six-layered cortical mantle that covers much of the brain and is responsible for higher perceptual, motor, and cognitive functions. It is a prominent characteristic in mammals and so is thought to be a relatively recent evolutionary adaptation, which is how it came by its prefix neo or “new” cortex. Thus, the prevalence of neocortex in the human brain is indicative of our species’ outsized investment in cognition.

Of course, brain size, or even neocortex size, is still a coarse measurement. The volume of neocortex would apply not only to cognitive control but to most other functions in the brain, from lower-level sensation and perception to action. Attempts to find more specific attributes that differentiate the human brain from other species have been more controversial. At the center of these debates has been the size of the prefrontal cortex in humans relative to other primates.

You might think that this debate should be relatively easy to resolve. You grab a few brains that we neuroscientists always have on hand, likely floating in vats on our shelves. You measure the prefrontal cortex in humans versus that in other animals. You correct for overall brain volume. Voilà. You publish.

But it turns out this task is not so simple, because actually defining the prefrontal cortex is no trivial matter. In fact, there are no universally agreed anatomical definitions that say what is and what is not prefrontal cortex. A further problem concerns the folding of the brain. Given that the neocortex is more convoluted in humans than in other species, any attempt to measure its volume must account for the different folding patterns. Ideally, one should be able to distinguish between the ribbon of cell bodies on the mantle of the cortex, termed gray matter, and the connections between cells that run through the center of the cortex, termed the white matter.

To resolve this controversy, neuroanatomist David Van Essen and his team at Washington University scanned 60 humans, 29 chimpanzees, and 19 macaque monkeys using magnetic resonance imaging.[46] They then inflated the brain images to align them to each other and measure them. “Inflation” isn’t literal here, though the image of David Van Essen blowing up a bunch of brains like a balloon (and pausing to catch his breath now and again) does bring a smile. Rather, it refers to a mathematical procedure that turns a bunch of wrinkly brain images into spheres with smooth surfaces. With the images inflated this way, scientists can use the geometry of spherical surface area to align and measure multiple brains, avoiding the challenges of complex folding patterns.

Using this approach, Van Essen’s team worked to define the prefrontal cortex, and the results were unequivocal. Humans have a disproportionately large prefrontal cortex relative to that of the other primate species. As a proportion of cortical gray matter, human prefrontal cortex was around 90% greater than in the macaque monkey and around 20% greater than in the chimpanzee. For white matter, this difference was even more striking. Prefrontal white matter volume was around 140% greater in humans than in macaques and around 70% greater than in chimpanzees. Thus, the human prefrontal cortex is greatly expanded relative to that of our nearest mammalian relatives. It is reasonable to suppose that this evolutionary expansion underwrote our species’ particular capacity for cognitive control.

The observation that white matter is even more expanded than gray matter is particularly intriguing. The gray matter is made up of neuronal cell bodies, whereas the white matter is made up of connections between the neurons. Thus, a greater expansion in prefrontal white matter in humans indicates that it is the disproportionate number of connections between neurons, as opposed to just more neurons, that distinguishes our evolutionary path from those of our nearest primate cousins. Further, this observation fits well with reports that, across species, as one moves toward the anterior forebrain, the density of neurons in general decreases relative to the back of the brain, while the synaptic machinery used for communication between neurons seems to increase. And this pattern is particularly prominent in humans.[47]

As brains became more interconnected overall, the connections in these prefrontal areas may also have evolved toward a hierarchical organization that is useful for control, as we will discuss further in chapter 4. Neuroanatomists have proposed two major trends of evolutionary change in primates, from older to newer areas, one running ventrally, and one running dorsally. Both trends exhibit a similar pattern along their front-to-back axis. Evolutionarily older areas in the back of the frontal lobe tend to connect more broadly around the brain, including to areas related to sensation, memory, emotion, and planning.[48] More recent areas in front, by contrast, have comparatively more restricted connections, connecting primarily within the frontal lobe itself.

By this definition, the anterior prefrontal cortex is the most recent architectonic zone. This region also corresponds to the area of the frontal lobe identified in earlier work by Van Essen as the cortical area of high expansion in humans relative to that in the macaque monkey.[49] As we will discuss in chapter 4, this anterior region of the prefrontal cortex may be critical during tasks that have deep goal hierarchies, needed for compositional action, and also for interfacing with the memory systems used for episodic future thought. This unique, recent expansion is quite notable, then, given our preceding discussion. The region is well placed to have supported interactions between systems for episodic future thought and cognitive control.

To summarize, then, the expansion in human intelligence likely came along with a large expansion in the human brain, particularly the prefrontal cortex and its associated network of association areas in parietal cortex. As we will see in later chapters, this hierarchical architecture may be well suited to supporting cognitive control, particularly acting according to multiple goals that differ in their abstraction or that prevail over different timescales.

The Ponderous Adaptation

It is clear that by 60,000 years ago—and likely much earlier—humans were busily getting things done in ways and varieties that none of their ancestors had done before. They were doing so using bigger brains and a more elaborated prefrontal cortex. A key question, however, is, what pressing need did a function like this meet? In other words, what pushed episodic future thought and compositional action control to this place on the evolutionary tree? What advantages did our ancestors with better cognitive control gain that led them to survive and reproduce such that their traits came to dominate the population?

At first glance, it seems obvious that a more effective cognitive control system and a capacity for general intelligence conferred any number of competitive advantages on our ancestors. Outwitting other animals, building better tools, thriving in more varied environments, sustaining larger populations, and so forth are major adaptive advantages.

However, evolution is not a ladder toward an endpoint. Nature has no goal it has set out to achieve; it is not targeting human intelligence. Our general intelligence works for us, but in evolutionary terms, we are just as successful as any other species alive today. These species have all found ways to survive and reproduce, and they have done so with evident success in their niche. General intelligence and language are our particular adaptations. They are our way of surviving and came about in their particular form because of constraints not only in the environment but also our own system. For humans, then, there was a history of costs and adaptations to those costs that yielded new advantages, but also new costs. The ultimate progression of these adaptations, costs, and further adaptations was presumably the cognitive control capacity we have now.

One version of events might go something like the following. At some point 3.5–4 million years ago, our ancestors moved from trees to the ground and became bipedal. This freed up their hands for toolmaking. They started to get smarter at this point, too. Maybe this was because they were routinely managing deeper goal hierarchies to make better tools to use in their free hands. Maybe it was because with increased manual dexterity, expansions in control over elaborate motor plans were required. Maybe it was because they were on the ground and vulnerable in new ways. Maybe it was all these reasons. However, in early hominins, like the genus Australopithecus, of which the famous “Lucy” was a member, it is evident there was selection for a bigger brain and the smarts that come with it. This began the trend toward the expanded prefrontal cortex seen in modern humans.

Along with a bigger brain came a bigger head in proportion to body size. Given the particulars of childbirth, a bigger head probably led to children being born altricial, meaning immature and helpless, relative to earlier species of ape. These altricial kids had to be carried around until they had matured enough to move, eat, and so forth on their own.

Carrying a kid around had its own costs. Some of these could have been solved by new forms of social cooperation. Indeed, social development itself, in terms of both social cooperation and deception, likely aided in selection for higher intelligence, including considering more and varied hypothetical futures or anticipating others’ responses to one’s own actions. In interacting with others, our ancestors could work toward shared goals, engage in cultural transmission, and outmaneuver, outwit, and outsmart other animals and one another.

Of course, this big brain they were carrying around needed energy. It is estimated that the human brain consumes around a fifth of our total energy supply.[50] So, for their body size, our ancestors had to consume more to feed their big brain, putting pressure on more and varied foraging strategies. This included new kinds of extraction methods using tools.

So-called extractive foraging is used by other great apes, too, like chimps, but the fossils left by our ancestors suggest they brought extraction and foraging to a new level.[51] They were cracking, digging, opening, scavenging, hunting, gathering, and processing in ways that no other species had done before, at least all at once. They were good at getting the food that few other animals had evolved to forage. Or if there was competition, they figured out ways to get it better, faster, or after. Thus, they had access to more and varied sources of food, allowing them to support larger populations of humans, which of course required even more social coordination.

Importantly, however, the demand to find new sources of energy may not have been the only pressure that shaped our cognitive control system. Investing in planning and episodic future thought as a survival strategy also incurs a cost in both time and opportunity.

Imagine you are foraging for berries in a place where berries are scarce. You don’t want to eat just every berry you find, because in the future you might not find any when you really need them. You might eat a few berries to meet your immediate needs, but then you start saving the rest for later. Deciding that your future needs outweigh your current ones—to say nothing of resisting the urge to eat that juicy berry right now—would require the cognitive control system. So, there is a clear advantage to both foresight and control in this setting.

There is also a cost, however. Control takes time, and time can be important. Thus, there is also an advantage to indiscriminately wolfing down every berry you find, to the degree that doing so allows you to be fast. This speed might be particularly important, for example, if there are other hungry berry-eaters about who might eat your berry while you merely ponder what to do with it. This trade-off between a slow and flexible control system and a fast and rigid automatic system is sometimes termed the dual systems framework, because it emphasizes two competing systems that differ in their speed versus flexibility.

In an insightful line of work, David Rand at Yale University, Damon Tomlin at University of Colorado, and Jon Cohen at Princeton University applied this dual system framework directly to the question of population dynamics in an evolutionary system.[52] They wrote a simulation with a population of agents living on a grid world who confronted this same berry dilemma. These little agents needed food to live and reproduce, and the food could sprout randomly on some number of the grid tiles. When the agent came upon a tile containing food, it could consume the food for energy. But the amount of energy gained by eating diminished as a function of its overall energy level. In other words, if the agent is starving, it gains more energy from consuming food than if it is already full.

Importantly, there were two kinds of agents in this world. There were “automatic agents” who would quickly eat any food they encountered. They did so regardless of their current level of energy. These automatic agents competed for resources with “controlled agents.” Controlled agents could decide whether to eat the food immediately or store it for later, based on an optimal balance between the energy gained by eating the food now and the likelihood of having food later.

The details of this decision rule are not really important. Rather, what is important is that the controlled agents would have a more stable energy supply over time owing to optimal consumption and storage strategies. However, making this optimal decision takes processing time. So, if a controlled agent and an automatic agent meet on the same food square, the automatic agent will eat the food, and the controlled agent will get nothing because of the time cost of controlled behavior. In other words, while the controlled agent is sitting around pondering the optimal use of the berry, the automatic agent just scarfs it down.

The simulation led to some interesting and perhaps surprising results. First, and not surprisingly, the controlled strategy was the winning one in worlds in which food was scarce. Agents who made optimal decisions about whether to store or consume tended to have a higher average energy level over time and so had a higher rate of reproduction. Consequently, the trait of controlled behavior came to dominate the overall population.

However, when there was significant competition, this pattern changed. Simulations were run with populations large enough to make it consistently likely that more than one agent would occupy a food source. Under these circumstances, the population of automatic agents grew in relation to that of controlled agents, and in some scenarios controlled agents were even driven to extinction.

In this and follow-up work, Rand and Cohen have argued that these trade-offs lead controlled and automatic behaviors to oscillate. Control has its advantages in planning and efficient use of resources, and this success can yield increased population and resources. But abundance and overpopulation are the very circumstances advantageous to the reckless speed of the automatic agent. Thus, successful controlled agents may inevitably construct a world that leads to their own demise.

Rand and Cohen even speculate that this dynamic could explain apparent oscillations of human history wherein great civilizations repeatedly rise and fall. Sophisticated planning and technological innovation enabled by cognitive control lead to the rise of civilization, but the abundance that ensues licenses freeloading automaticity that competes well in these circumstances. However, this automatic behavior fails to adapt to change and ultimately collapses under its own weight.

These are provocative speculations, but there are reasons to be skeptical about the extension of this idea as a theory of human history. For instance, controlled versus automatic behavior should not be placed within this Manichean framework, such that controlled behavior is always good or rational, and automatic behavior is always bad or irrational. Tragically, many people engage in highly planned, premeditated, and fully controlled behaviors that are selfish, foolish, and/or evil and destructive to their society.

For example, lots of innovation went into concocting the various complex financial instruments that precipitated the 2007 housing market crash. Someone had the notion that packaging a bunch of subprime mortgages together and marketing them as a new thing called a “collateralized debt obligation” would make them a lot of money. They were probably right. And doing so clearly required human-level intelligence and cognitive control to implement. I have yet to hear of a chimpanzee selling subprime mortgages. But this innovation was undeniably bad for our society, costing taxpayers billions, and many people in our society their home and livelihood. Cognitive control is not a moral construct. Control makes many good things happen in a society. Control also leads to novel and well-planned acts of corruption, conspiracy, terrorism, and genocide on a scale not possible for any other species.

Conversely, many highly tuned automatic actions lead to considerable good and are arguably achievable only in advanced civilizations that furnish the infrastructure, resources, and/or leisure required to train them. We have to look no further than the high-level crafts, skills, and athletics found in most great civilizations. Mastery of this kind requires years of shaping and skill building to automate the subtasks that make these feats possible.

Setting aside the commentary on human history, however, the central insight from the Rand, Tomlin, and Cohen simulations is quite valuable for our discussion. Sophisticated plans take time to generate and are costly to implement. Indeed, the more advanced and sophisticated a plan, the more demands the plan places on a control system to carry it out. If there is any cost to a loss of time or an increased chance of error, then this will offset the advantages of planning a new, but optimal, behavior. This cost of implementation would then be a selection pressure that control systems could be adapted to solve.

To illustrate, consider the time cost that is baked into the Rand, Tomlin, and Cohen simulations. This cost might be taken to reflect the time it takes to plan and consider whether to eat the food. A control system could be helpful in offsetting this planning cost by, for example, allowing an agent to plan its actions out in advance. For example, if our little controlled agent has a small working memory that is capable of holding one rule in its head, then it can spend a turn on a tile without food, planning what to do with the next food tile it finds. It can load the simple rule “if food, then store” into its working memory and forget about the complex decision rule that led to this implementation. It will act to store the next food it encounters, and it can do so quickly, thus competing effectively head-to-head with automatic agents it encounters. As we will see in chapter 3, working memory has control operations, called gates, that might serve precisely this purpose.

Indeed, in this example, as one increases the capacity for control, more elaborate plans might become more feasible, both to construct and to implement efficiently. For example, imagine that our planning agent wants not only to decide whether to eat or store food but also to take its actions depending on the ripeness of the berry. It now has a branching action plan based on more than one condition of the world: the amount of food and the color of the food. It can make this plan ahead of time, but if it can hold only one condition (amount or color) in working memory, then it cannot make both of these decisions at once. It will be faster than a planning agent with no working memory, because it knows to immediately assess the ripeness when it sees a berry, but it will still need to replan whether to eat or store the berry because it can’t hold both conditions in mind. Thus, a third planning agent with a more advanced control system that can keep two conditions straight in its working memory will be faster than either of these other two planning agents.

Control allows us to compensate for planning costs and so efficiently and effectively implement more sophisticated plans. It does no good to plan eight moves ahead in chess, even if we can think of all the possibilities, because we also can’t hold all the action contingencies in our working memory to enact this plan quickly. We will need to stop and replan regularly. In sum, there is little advantage to deep planning if we can’t efficiently implement the plan.

From this perspective, then, we might hypothesize that cognitive control and episodic future thought evolved together because of the pressures they placed on each other. Growth in a capacity for cognitive control may have allowed more elaborate plans and deeper goal trees to be entertained when planning. However, implementing these more elaborate plans would have resulted in costs in time and processing that would pressure selection for a higher-capacity control system to accommodate them efficiently. This in turn would permit yet more elaborate plans, and so forth. This push-pull dynamic could have led to increasingly higher capacity for control and episodic future thought.

Of course, this fundamental relationship between planning and implementation goes beyond our own head. We can also expand our capacity for both planning and implementation by constructing tools. At some point, our ancestors invented writing and symbolic systems that expanded and situated the capacity for planning and implementation in the world around them. They no longer needed to hold all their ideas in their head. They could write them down or summarize them with symbols. They could think on paper. This expanded capacity could then be used to invent even better technologies and then to implement these new plans. Indeed, this process is still ongoing, marked most recently by the invention of the computer. The computer drove an explosion of new technologies in the last 60 years to rival that in the Upper Pleistocene. If all historical records were lost due to some future cataclysm, one could imagine some subsequent archaeologist looking at the late twentieth century and wondering what new trait evolved in humans that enabled this burst of innovation. But no such thing was needed. We invented a new technology for inventing new technologies, and the result was an explosion of innovation.

I think it is likely that our Homo heidelbergensis kneeling in that gravel pit in Boxgrove, England, was able to refine its hand axe because its cognitive control capacity was advanced beyond that of its ancestors. It was managing a deeper goal hierarchy than that of earlier species to build its axe. These enhanced capacities would have also allowed it to engage in more and varied future thought and so to envision the advantage in setting aside a pile of flakes to sort through later. Maybe it envisioned a better axe, and that is why it came to Boxgrove in the first place. These capacities would not have reached those of modern human levels yet or a point that could lead to the explosion of innovation observed in the Upper Pleistocene, but they did allow behaviors not seen in other primate species.

Based on the evidence reviewed here, we can speculate that cognitive control arose in tandem with a capacity for episodic future thought. Planning has costs. We have highlighted one such cost here, the time it takes to plan. We will encounter additional costs and trade-offs that come along with flexible and compositional action in the subsequent chapters of this book, starting with chapter 3. These trade-offs are the price we pay for our mental flexibility, and they surely shaped our control system.

As cognitive control developed, it could manage and reduce these costs. It allowed more elaborated plans to be generated, which in turn selected for greater control to implement them. Accordingly, in the brain, we see a development and elaboration of the prefrontal cortex that progressed from more primitive limbic areas, supporting memory and emotion, and motor areas, supporting basic action. This elaboration of the prefrontal cortex wired local connections in hierarchical fashion that provided an organization for control of itself.

Language was also an important part of this process that we have not touched on much here. As it evolved, language likely made essential contributions to the interaction between planning and cognitive control more broadly. Episodic future thought could be enhanced only by an ability to talk about one’s plans to others or to oneself. This allowed for cultural transmission of goals and plans and procedures for doing things that had been done before or might be doable as a team. But again, once a plan was transmitted and an image formed in the head, the control system was required to ensure the plan could be implemented efficiently. As noted in chapter 1, it is not trivial to take a verbal instruction and just do it. It requires a general-purpose cognitive control system to realize that instruction as new action.

If these are the evolutionary roots of cognitive control, then understanding it requires recognizing this fundamental interaction between generating goals and implementing them. Future planning or producing a generative model of the world allows one to be nimble in many situations but also comes with costs on time and the potential for errors. Cognitive control evolved to minimize the costs of this hypothetical thought to the degree that doing so led to adaptive advantages. Thus, the mechanisms of cognitive control cannot be considered in isolation from planning, conceptual, motivational, and goal systems. Cognitive control is not a system for merely inhibiting animalistic impulses or being more rational or doing multitasking. Cognitive control evolved to make the conceivable become realizable given the particular organization of our brain and our action systems.

CHAPTER 3

The Stability-Flexibility Dilemma

Imagine that you are driving to a friend’s house when your cellphone buzzes, signaling an incoming text. For most of us that buzz triggers an urge to check our phone. Of course, the dangers of doing so while driving are also well known. The US Federal Communications Commission has estimated that more than 1000 people in the US are injured and 9 are killed each day owing to cellphone use while driving.[1] Several states have passed laws banning or severely restricting cellphone use in cars; almost all ban texting explicitly. Yet, it is also estimated that at any moment during the day more than half a million drivers are using their cellphone or other electronic devices while driving.

Yes, some of these people who text while driving may simply not know about the dangers of distracted driving, or, worse, they may think themselves capable of handling it. But it is also likely that many people are fully aware of the risks, and yet the buzz of an incoming text is a stimulus they just can’t resist. This is likely because those thousands of calls and texts they’ve received over the years have actually been thousands of little lessons for their brain. They are learning events that have trained their brain to link the buzz of an incoming text with the action of looking at their phone and the social reward it brings. For some people, the reward of checking their phones doesn’t even require the buzz of the phone. They have learned to check their various social media sites at regular intervals, whether they are driving, eating dinner, or in a face-to-face conversation with another person. Indeed, social media sites weaponized this reward function when they added the “Like” button. We now check and recheck our posts of vacation pictures and adorable pets with the frequency of a rat pressing a lever for cocaine.

To avoid texting while driving, our cognitive control system must override our learned impulse to check the phone. Somehow we need to use our context, in this case driving, to change how we usually behave with a buzzing phone. We might outright stop ourselves from looking at the phone or simply choose to refocus on the road or ask someone else in the car to check the phone for us. Using a context to choose a less frequent or alternative course of action is the most basic expression of cognitive control. What basic mechanisms in the mind and brain allow us to succeed in cases like this?

In this chapter, we will address this question, first discussing the central position of working memory in situations like our driving example. Then, we will discuss how an ancient circuit for motor control was co-opted by the brain to manage working memory and thereby became the fundamental circuit for cognitive control function.

Working Memory and Control

In a seminal paper published in 1990, Jon Cohen, Kevin Dunbar, and Jay McClelland reported a simple neural network model that could perform the Stroop task.[2] Recall from chapter 1 that in the Stroop task, when the ink color of a written word does not match the color named by that word, we have difficulty naming the ink color, usually evident in a slowed response time.[3] Conversely, reading the word does not produce the same difficulty, even when the word and ink color are mismatched. This asymmetry emerges because we have more experience reading words than naming colors, so word reading is more automatic. Thus, we can think of the Stroop task as a laboratory model of the choices we make while driving. As in overriding our compulsion to check our text message, when our context is the color-naming task, we must overcome the automatic tendency to read the word in order to name the color instead. We can do it, but it takes us more time.

To explain our ability to do this, Cohen, Dunbar, and McClelland proposed the guided activation model pictured in Figure 3.1.[4] From here on, we’ll refer to this model as the CDM model, after its authors. CDM is an example of a theoretical tool called a computational model. Computational models are formal, mathematical expressions of a theory, often in the form of a computer program. They allow scientists to be explicit about the assumptions their theory makes and to verify how well their theory matches the phenomena they seek to explain.

FIGURE 3.1. A depiction of the guided activation model of Cohen, Dunbar, and McClelland (1990). An input layer at the bottom contains neurons (dark gray circles) that represent the colors green ink and red ink on the left, and the words “green” and “red” on the right. These connect to neurons in the hidden layer in the middle, represented by the lines. And these neurons connect to two response units at the top that represent the verbal responses “green” and “red.” A task demand or context layer represents the color-naming or word-reading task. The thickness of the lines represents the strength of connection between neurons.

CDM is a particular kind of computational model called a neural network model, meaning that its computations are inspired by the kinds of interactions real neurons have with each other. CDM is simpler than the convolutional neural nets that you hear about at places like Google Deep Mind, but the basic principles are similar. So, before we can understand how CDM implements cognitive control in the Stroop task, we need to cover a little background on how neural networks like CDM compute.

A neural network is made up of simulated neurons that receive input from other active neurons that connect to them. The more input a neuron receives, the more active it will become. The relationship between how much input a neuron receives and how active it is often follows an S-shaped function. This means that when there are small-to-moderate amounts of input, the neuron won’t be very active. But at some point, when the input is great enough, the neuron will rapidly gain activity to a maximum. The more active a neuron is, the greater the input it delivers to target neurons to which it is connected. Once its inputs are great enough, that target neuron passes its input to another target neuron to which it is connected, and so on.

The propagation of these inputs from one neuron to the next is how a model like CDM computes. It allows a particular pattern of activity across one set of neurons to reliably and consistently produce a pattern of activity across a set of target neurons. This propagation of activity patterns is akin to what the brain is doing all the time. It is how we go from a pattern of neural activity across our perceptual systems corresponding to, say, an ice cream cone we see, to a pattern of neural activity across our motor system that eats it.

Importantly, the strength of the input delivered from one neuron to another will depend not only on activity levels but also on the strength of their connection. This connection strength is sometimes called a synaptic weight, in reference to the synapses that connect real neurons with each other in the brain. Obviously, the stronger the synaptic weight, the more likely that the sending neuron can elicit high activity in the target neuron. At the scale of the whole network, synaptic weights determine how a particular pattern of activity in one population of neurons will produce a consistent pattern of activity in another population of neurons. In other words, these synaptic weights are how models like CDM store information. Thus, to understand how CDM solves the Stroop task, we need to examine how its various neurons are connected to each other and how their synaptic weights are determined.

CDM has four basic groupings of neurons, called layers, as depicted in Figure 3.1. At the bottom is the sensory layer. This layer is the model’s rudimentary perceptual system. It has neurons that fire to specific features the model senses in the world. Of course, CDM’s simulated world is quite simple. It consists only of the words “red” and “green” that appear in the ink colors red or green. So, its perceptual system does not need much sophistication to cover every situation it might ever encounter in its world. There are individual neurons for the ink colors red and green and the words “red” and “green.” Each neuron will fire when its feature is sensed. For example, if the model encounters the word “green” written in red ink, the corresponding neurons for the word green and the ink color red will fire. That pattern of activity across the sensory layer is the model’s representation of the word green written in red.

At the other end is the response layer. This layer has neurons corresponding to the spoken responses the model could make to seeing a colored word. For this model, there are only two responses one ever makes, either say “red!” or say “green!” When one of these neurons fires, the model has given the corresponding verbal response. Further, the response units compete with each other, so that both cannot be active at the same time. This makes sense. You can’t say “red” and “green” simultaneously; why should we expect the model to do so?

Between the sensory and response layers sits an intermediate layer, sometimes called the hidden layer. The hidden layer receives its input from the sensory layer and passes its output to the response layer. Thus, you can think of the propagation of activity from the sensory to the intermediate to the response layer as being a pathway linking a stimulus to a response. In other words, when the model sees the word “red” and then says “red!” a specific set of neurons will be active in the sensory, then the intermediate, and then response layer. This specific propagation of neural activity corresponds to the red-word response pathway, and it will be different from the green-word response pathway or the red-ink-color response pathway. Indeed, the model will have a separate pathway for each mapping of stimulus feature to response. The hidden layer is needed because without it the model could not learn to form these separated pathways, such as for either the green word or the green color but not both.

A final critical feature is that these pathways are learned by the model, not programmed by the model builder. That would run afoul of our pesky homunculus problem from chapter 1. Rather, CDM learns the connection strengths through experience with seeing words and colors and making responses.

Learning occurs through a process called back propagation. On each learning event, the model is shown an input, like the color red, and is allowed to produce an output. “Green!” the untrained model guesses, for example. The difference between the response it gave and the one it should have given is an error, which can be expressed as a number, a difference between the desired and elicited activity pattern. For the model to learn from this mistake, the programmer adjusts the synaptic strengths along the pathway that produced the response in proportion to the magnitude of this error and their contribution to producing it. This process is repeated over and over again until this error is reduced. From the gradual adjustment of synaptic weights to reduce error, the model can learn how to map each stimulus input to the right response without needing a programmer to set them that way. The model learns and stores the response pathways for each stimulus feature in its connection weights.

We now have a simple model that can respond “red!” or “green!” depending on the color of a word or the color it names, but we are not quite to the Stroop task yet. First, a feature of the Stroop task is the predominance of word reading over color naming. To implement this in the model, the model gets more experience with word reading, similar to the way people do. During its training, the model gets to read a word many more times than it names a color. Doing so results in stronger synaptic weights along the word-reading pathways than the color-naming pathways. This means that the same level of input strength for both colors and words will produce a stronger, faster response along the word-reading pathway than the color-naming pathway. All else being equal, then, CDM will always read the word rather than name the color and will do so without interference. It won’t matter if the color word and ink color are the same (the word “green” written in green) or different (the word “green” written in red). The response will be based on the word, and there will be no interference.

What happens if you want the model to name the ink color? As it stands now, the model can name colors only if there is no word stimulus. If a word is present, the model will always name the word because those are the stronger, prepotent response pathways. So, to perform the Stroop task, the model needs cognitive control, and this is where that fourth layer that we haven’t discussed yet comes in.

The fourth layer is termed the task demand layer. This layer has task units, which are neurons corresponding to whichever task is currently instructed, either word reading or color naming. When the model is doing one of these two tasks, then the neuron representing that task is active in the task demand layer. This task demand layer delivers input to the hidden layer. As a consequence, whatever task demand is active in the task demand layer has the opportunity to directly influence the pathways linking the stimulus and response.

With experience, the model can learn that input from the color-naming neuron in the task demand layer to the hidden layer neurons along the color-naming pathways is crucial for success. This is because activation of this task in CDM’s task demand layer provides the additional input needed for the color-naming pathway to win its competition with the stronger word-reading pathway. Note that the converse will not be true. CDM will not learn that task demand input for word reading is necessary, because the model will perform word reading successfully regardless of the state of the task demand unit for word reading. Further, though the model will ultimately produce the correct response when color naming, the competition from word reading will take a while to overcome. This means the model will take longer to respond on these color-word interference trials. Humans performing Stroop behave similarly, mostly saying the correct name of the ink color, but just taking longer to do so.

Thus, we now have a basic mechanism for cognitive control that can change its behavior according to the task demands and behaves similarly to humans performing a Stroop task. And importantly, there was no need for a homunculus. The model learns both the response pathways and the incorporation of task demand units needed for success.

Models like CDM intentionally simplify what would happen in a real brain. For example, no one is proposing that the brain relies on single neurons to stand for things like the color red. Furthermore, our brain has more than just a back propagation algorithm for learning. But these differences between the model and the brain are not important for the goal of this theoretical investigation. If you like, you can think of those single neurons as representing whole populations of cells in a more complex system. This doesn’t matter. The advantage of models is that they provide key insights into the kinds of minimal mechanisms we need for control, and there are several such insights to be gleaned, even in this simple case.

First, the difference in strength across various action pathways highlights in a rudimentary way the continuum of automaticity in our own action repertoire. Often, we think of controlled and automatic behaviors as a strict dichotomy, as though we have two brains for doing two different things. One brain does things slowly but in the service of goals, and the other does things quickly and without thought. We use one brain when we make our morning coffee and the other when we make a Vietnamese egg coffee for the first time. However, this dichotomy is likely wrong.

The reality is more continuous, much like the variable strengths across pathways in CDM. Some actions we perform are highly automatic, nearly habitual, and these have strong pathways from stimulus to response. Others are less automatic and so are a bit weaker. And some tasks require entirely new combinations of actions and are very weak. When multiple pathways are elicited at once but are in conflict because we can’t perform them all at once, the weaker pathways will lose unless they receive support from the control system. Word reading will always dominate color naming, unless the control system steps in. If there is additional information about the situation that favors the weaker action pathway, such as the current goal or a task demand, then this input can provide a supporting signal that will boost the weaker pathway and allow it to win its competition. In so doing, we have contextualized the link between our situation and our behavior and so have achieved the most basic kind of control.

A second notable feature of the CDM model is that the pathways from stimulus to response do not run through the task demand layer. Rather, this layer acts like a train operator, sending its supporting influence to select the track along which the input from stimulus to response proceeds. This is only important when there is overlap and conflict. When the pathways do not compete or when the appropriate pathway is strong enough on its own, the model does not need this top-down support.

This situation is analogous to the kinds of intact automatic behaviors observed in patients with dysexecutive problems like those seen in chapter 1. The patient who got into Dr. Lhermitte’s bed obviously did not have a problem carrying out this action. And when the patient performed it in his own home each evening, it was the appropriate thing to do. However, when this strong pathway conflicted with the weaker actions required by the novel context of his neurologist’s bedroom, the weaker, correct pathway lost the competition to the stronger, incorrect one. Whether this was because he was unable to keep this context in mind or was unable to deploy this context in a way that was usable as a control signal, the patient could not use the context to bolster the correct pathway in its competition.

A third insight from the CDM model is that our task context must be actively maintained somewhere in our head to support weaker pathways. In the model, input from the task demand units is needed to bolster the weaker color-naming pathway until it wins its competition. This input is needed even if the relevant contextual information is no longer present in the world and so can’t be acquired through the senses. If you are told at the beginning of the experiment to do the color-naming task, you don’t have to be told again for you to use it as a contextual signal. This is because you can maintain this task demand in your memory.

It follows that sustained maintenance of contextual information is a key feature of cognitive control. Working memory is the cognitive system we use for this kind of active maintenance of useful information. As introduced in the last chapter, working memory holds a limited amount of information active and accessible for short periods of time. It is distinct from long-term memory, like episodic memory, that stores information over very long periods, but in a quiescent state. Rather, information in working memory is active in mind, but it is also rapidly lost. Once information is active in working memory, we can manipulate and operate on it, and can even use it to produce new ideas and thoughts.

For example, add 31 and 24 in your head. Did you come to an answer? Now, reflect back on the process you went through. You needed to first hold the numbers 31 and 24 in mind, in your working memory. Then you worked through whatever steps of addition you know in your head, updating and replacing the numbers as you went. Eventually, you came to your answer, not because it was printed on this page anywhere but, rather, it arose in your working memory from the operations performed within working memory itself.

Working memory also provides a partial answer to our temporal abstraction problem introduced in chapter 1. Recall that temporal abstraction occurs when we respond on the basis of goals or plans that are not immediately cued in our environment or that need to hold sway over extended periods of time. Working memory is a means of bridging gaps in time. We can collect information through the senses, operate on that information to conceive of new thoughts, ideas, and goals, and then we can use this information to respond when the time is right. So, the CDM model places working memory front and center in cognitive control function because it is a means of actively maintaining task demands.

How is a working memory for task demands supported in the brain? In another highly influential paper, Jon Cohen and neuroscientist Earl Miller proposed that the prefrontal cortex is uniquely suited for this working memory function.[5] It is well known that damage to the prefrontal cortex produces problems with cognitive control, so it makes sense that they focused on this part of the brain. But the profound implication of their new hypothesis was that these patients’ deficits stem from a loss of working memory for task contexts.

In support of their hypothesis, Miller and Cohen cited a range of evidence relating properties of the prefrontal cortex to properties of the CDM model. They noted that electrophysiological recordings in the macaque monkey find neurons in the prefrontal cortex that fire selectively to specific bindings of stimuli and responses, and not just the stimuli or just the responses. In other words, prefrontal cortex neurons are responding to the rules of the tasks the monkey is performing. Further, prefrontal neurons maintain this rule information actively whenever those rules are relevant, a key requirement for working memory. Thus, rule coding by prefrontal neurons shares an attractive similarity to the task demand units found in the CDM model.

The hypothesis that the prefrontal cortex supports working memory in the service of cognitive control has been widely influential. And in the years since Miller and Cohen’s paper, there have been several refinements of this basic proposal. For instance, we now know that the prefrontal cortex is not the only region in the brain to exhibit evidence of working memory.[6] Indeed, short-term maintenance of all kinds of useful information is evident throughout the cortex, including areas conventionally associated with primary sensation and perception. Heightened activity or a higher likelihood of firing in these cells can persist after an eliciting stimulus has arrived through the senses and subsequently departed from the environment. As such, this activity can support a short-lived memory trace for the sensory information coded by these neurons that can be used by other systems for ongoing processing. However, what might be unique about the prefrontal cortex is not working memory in general, but working memory for task contexts—the active maintenance of rules, goals, task features, and any other information that can influence which actions we take and when. These task demand features are crucial for cognitive control.

Further, we now know that the prefrontal cortex does not work alone to maintain the task demand features needed for cognitive control. Rather, the prefrontal cortex is part of a broader network of brain regions that serve this role. Indeed, more than one subnetwork, involving different areas of the prefrontal cortex, may be crucial for control. Nonetheless, fMRI studies have identified a consistent network of brain regions that are active when people engage cognitive control.

One of the clearest delineations of this network came from an experiment published in 2013 by Evelina Fedorenko, John Duncan, and Nancy Kanwisher.[7] In this study, people performed a variety of small tasks while being scanned with fMRI. The tasks differed in their specifics; some involved remembering words, others involved remembering spatial locations. Some required solving math problems. There was even a Stroop task. Importantly, however, each task included an easy condition and a hard condition. The definition of easy and hard was simply based on performance: the harder conditions would take longer and provoke more errors than the easy conditions.

Despite the wide variety of tasks people performed, the hard conditions of each task yielded increased activation in a broad network of brain areas relative to the easy conditions, as shown in Figure 3.2A. This network included a large area of the lateral frontal cortex, but it also included other regions in the frontal and parietal lobes. Because this network was more active for demanding tasks, regardless of the specific demands of the task, Fedorenko, Duncan, and Kanwisher referred to it as the multiple demand system. However, in this book, we will broadly refer to this network as the frontoparietal control system.

It makes sense that a network involved in cognitive control would tend to be more active for the difficult conditions of any task. Most theories of cognitive control agree that difficult tasks require breaking a problem down into manageable steps and managing these or choosing less well learned response pathways. From the CDM perspective, difficult tasks will elicit weaker responses along the relevant response pathways, and so, greater contextual support from task demand units is needed for these pathways to settle on a response.

Strong evidence now links this frontoparietal network to cognitive control function. Damage to components of this network impairs performance on complex reasoning and problem-solving tasks that require cognitive control, and this deficit can occur even when other higher functions, like language, are preserved.[8] This network is more active when people must adapt to and follow new rules on the fly.[9] Furthermore, regions throughout this network maintain information about task rules.[10]

Thus, the frontal-parietal system broadly supports cognitive control function, but this does not mean it is an indivisible network that acts like the CPU of the brain. Likewise, it does not mean that this is the only brain region that supports control. We will see in later chapters that other networks, also involving prefrontal cortex, are important for cognitive control function and serve related but distinct functions. Furthermore, the frontoparietal control system itself can be divided into separable networks. For example, as depicted in Figure 3.2B, network analysis of the human brain indicates that at least one cleavage of the frontoparietal control system is between a primarily lateral frontal and parietal network and a cingulo-opercular network.[11] Further, there are additional subnetworks within the lateral frontoparietal network itself that interact when tasks require higher-order contexts. We will discuss these in chapter 4. Thus, the frontoparietal control system is a broad-based delineation of areas consistently involved in cognitive control, but important distinctions needed to understand cognitive control function in the brain remain.

FIGURE 3.2. Location and key landmarks in the frontoparietal control system. (A) Anatomical labels identify the key brain regions found by Fedorenko, Duncan, and Kanwisher (2013). (B)The shaded areas designate the distinct frontoparietal (lighter) and cingulo-opercular (darker) networks within this system, as defined through functional connectivity by Power et al. (2011) and Yeo et al. (2011). Adapted from Figure 1A in Gratton, C., Sun, H., and Petersen, S. E. (2018). Control networks and hubs. Psychophysiology 55(3), https://doi.org/10.1111/psyp.13032.

In summary, working memory for task contexts, supported by a frontal-parietal control network in the brain, is central to cognitive control. However, our discussion of working memory in cognitive control has so far left out one crucial feature. If working memory for task relevant information is key to enacting the right response pathways for behavior, then this also means that getting the right task information into working memory is crucially important.

In CDM, the task demands just arrive in working memory like any other input. Indeed, CDM’s working memory has only two units, corresponding to the two tasks, and the correct one is active at the start of each trial depending on which task the model needs to perform. This is fine for CDM, because the little world this model lives in consists of only reading words and naming colors. There is no need to be judicious about what to put into working memory or when to do so.

Our brain has a harder problem to solve, however. Recall the scaling problem from chapter 1. We live in a complex, multidimensional world with many potential tasks and contexts. Not everything we see or hear is a relevant task demand to be remembered, and, likewise, not every task demand we have in mind should act as an influence on what we do every moment. To make matters worse, our working memory has a puny capacity. By current estimates, we can keep only around three to four memories active in working memory at a time. After that, we start forgetting or confusing what we are holding in mind. So, if we want to use our pea-sized working memory to control our behavior, we need to be judicious about what we let into memory and when we let it affect our behavior. In other words, we need to understand how working memory itself is controlled.

Our brain’s mechanisms for controlling the contents of working memory are the operators by which the control system implements the various goals, plans, and rules that we cook up for ourselves; they are the key bridge between thought and action. Given their centrality to cognitive control, in the remainder of this chapter we will consider these mechanisms for controlling memory more fully and how they are realized in the brain.

Stability versus Flexibility

Like most academic scientists, I spend much of my day at my desk working on my computer, usually writing. Perhaps I’m working on a paper or a grant or trying to finish a book chapter to meet a publisher’s deadline. I use an Apple machine and so also have Mac Mail as my email client. The Mac Mail icon hangs out in a dock at the bottom of my screen. As I work, the number in a little red badge on the Mail icon will increment ever higher telling me how many unread messages I have pending. This red badge can be a source of considerable distraction; it beckons continuously and becomes harder and harder to resist. However, if I want to finish my work on time, I must resist this other task, and maintain focus on what I am doing.

Maintaining task stability in the face of distraction is hard, and doubtlessly requires cognitive control. Indeed, where possible, we often try to structure our environment to reduce distractions. People disable their phone or the Internet. We can hide the dock on a Mac. Software is even marketed that will mandatorily restrict our computer to only one application or will lock out our email for a preset period of time. So, task stability is not the default but requires that measures be taken, whether external in our environment or internal through cognitive control.

Of course, I do not want to be too stable, either. Again, imagine I am working on my writing when a graduate student runs into my office with an emergency. Maybe our server just caught fire. I do not want to be so stable that I continue to doggedly write my manuscript while our whole data infrastructure burns down. I need to be able to stop what I am doing and shift to a new, higher-priority task. Flexibly shifting to new tasks as the situation demands also requires cognitive control, particularly when the task we are shifting from is engaging, or the one we are shifting to is not. Ask any parent who wants their toddler to stop playing in order to have dinner or take a bath. Suddenly, a three-year-old’s task stability can rival anyone’s.

Thus, there is a fundamental trade-off between stability and flexibility, and our cognitive control system faces the dilemma of deciding when to be stable and when to be flexible. Indeed, this is a basic computational trade-off that transcends any one realization. Be it the mind or a computer or a society, mechanisms that make that system more stable must also make it less flexible, and vice versa.

How does the brain confront this dilemma when controlling what we are doing? If the brain is to maintain stable states, such as sustaining a task demand like color naming in working memory, mechanisms for doing so are going to balance against the demand to flexibly update those working memory states. Too much stability in memory would mean that we might miss updating relevant demands to memory, and obsolete demands would hang around in memory too long, taking up space. I’d still be thinking about making my morning coffee long after it was drunk and gone. Conversely, a highly flexible working memory would constantly overwrite its own contents with whatever input signal impinged on the system at any given moment. Given the ever-changing world we occupy, it would be hard to maintain focus on anything for any reasonable period of time. We would quickly fill up our little working memories with the happenstances of the moment.

The brain’s solution to the stability-flexibility problem in working memory is to impose a division of labor between the mechanisms for stable maintenance of information and those for controlling access to the maintained information. These mechanisms that control working memory are called working memory gates.

A gate is a helpful metaphor for thinking about computation in a neural network like a brain. Gates exist in either of two states, open or closed. When they are closed, they prevent passage of information from one neural pathway to another. But given certain conditions, they are opened, and transmission proceeds.

To see how the brain manages stability versus flexibility, we can extend this gating metaphor to control over working memory. First, a gate is needed to control what information from the world is allowed into networks supporting working memory. When the gate is open, new information enters the prefrontal cortex from perceptual systems to be maintained in working memory. We have flexibility. When the gate is closed, irrelevant information is kept out of prefrontal circuits, and what is there is protected. We have stability. This type of a gate is called an input gate because it controls what gets into memory and what stays out.

Input gating is obviously adaptive in a world as rich and complex as ours. Innumerable inputs impinge on our senses, and many of these might signal distracting tasks. So, to avoid engaging that red badge on my Mail icon, an input gate can stay closed to keep that distraction at bay and the task it cues out of my memory. Conversely, an input gate can also open to update memory when new, important task information arises, such as my student asking for help.

Sometimes, contextual information is encountered that one wishes to hang onto for the immediate future but not act on just yet. For example, imagine you are in a heated conversation with a friend about politics. At one point, they mention something you believe to be untrue. The impulse to interrupt them right then and there may be strong, but doing so would be rude and likely counterproductive to your discussion. Thus, the prudent course is to hold the gist of your comment in working memory and wait for the appropriate moment to let it fly.

This example illustrates that not only is there an advantage to deciding what gets into working memory, using an input gate, but there is also an advantage to deciding when the information hanging out in memory gets to exert control over behavior. We will call this second kind of gate an output gate, as it controls the “output” of working memory. When the output gate is closed, information in working memory is maintained and protected, but it does not influence behavior. When it is opened, the selected information in memory provides contextual support to our action pathways. In essence, we have placed a gate between the task demand layer and the hidden layer in the CDM model. When that gate opens, the neurons in the task demand layer can deliver their input to the response pathways and so influence what the model does.

Getting the input and output gating right, in terms of what is gated in or out of working memory and when, may be at the very heart of efficient task performance.[12] The world is a dynamic place with information changing around us in both space and time. We also have a vast repertoire of automated action pathways available at different levels of granularity, from simple movements to full-blown sequences and tasks. Thus, the right gating policy will specify the flow among these inputs and precompiled outputs effectively, analogous to control flow in a computer program. The right gating policy permits the right task demands to exert their influence at the right times. The wrong gating policy will subject us to errors and slips, lost contextual cues, inefficiency, or inappropriate behaviors.

Identifying the right gating policy makes more concrete the elusive bridge between knowledge and action. Consider that instructions on how to perform even a simple task typically leave out the specifics needed to implement that task in your brain. “Welcome to Madrid airport. Signs will guide you to our connecting flight.” This is a helpful instruction, but to actually follow this rule and make it to your connecting flight requires setting up a complex policy of working memory gating. You must decide when to monitor for signs, where to look for them, and what to look for on them. You must maintain information provided by the signs to direct your turns until new information arrives while avoiding irrelevant information, and so forth. Travel is often particularly demanding on our cognitive control systems for precisely this reason. It places us in new environments where general rules must be given novel gating policies on the fly.

Even a simple rule linking a stimulus to a response, like “Press A when you see a 1,” must be translated into a gating policy to be implemented. Your brain must figure out that when A is seen somewhere, an input gate should open and hold that A as a context in working memory. Then, when it is time to respond, an output gate must be opened to allow the A to influence the choice of the response “1.” None of these ins and outs are expressed in the rule we were told, and getting them just right without knowing precisely how As appear and 1’s are pressed is challenging. This is partly why even the simplest tasks still take a few attempts to reach their most efficient and consistent level of performance.

As a scientist interested in these problems, it is hard for me to test people’s working memory gating in the Madrid airport or a similar real-world setting. However, in the lab, I can test basic working memory gating with simplified experiments. Cognitive neuroscientist Christopher Chatham worked with me to develop one such procedure, which we term the context-first/context-last task.[13]

In this experiment, a number, a letter, and a symbol are presented one at a time on a computer screen to volunteers, as shown in Figure 3.3. The volunteers are told that immediately after each series, their memory will be tested for the identity of the letter, of the symbol, or of both. The number is a cue that will tell them whether letters or symbols or both are the “targets” that will be tested on that series. So, for example, say the number 1 means remember the letters, 2 means remember the symbols, and 3 means remember both. If the sequence “1 … … A” is seen, then A would be the target to remember for the memory test.

In the preceding example, the number 1 provides a simple context that people can use to guide working memory gating. When they see the 1 first, they know that the upcoming letter must be remembered. They can translate that into a gating policy wherein the input gate is closed to any symbols that appear, and the gate is opened for letters, like the A. In this way, only the A is kept in memory until the test.

On other trials, however, Chris presented the number context last, as in “ … A … 1.” In this case, people would not know whether the or the A would be tested, so they would have to hold both the letter and number in working memory initially. Then, on the final presentation of the 1, they could select the A from within working memory to guide their response. This selection from within memory would use an output gate, choosing the A in memory to be the control signal and not the .

When people perform this task in the lab, they find the series with the number context coming first to be easier than when it comes last, because they can use input gating to limit working memory to only the relevant item. We will call this way of doing the task the input gating policy. When our volunteers use the input gating policy, they block the symbol and remember the letter, and so the load on memory is only one item. They don’t waste capacity on the irrelevant other item. By contrast, when the context comes last, they must hold both the letter and the number in mind until the context appears. As a result, people perform better when they use the input gating policy.

FIGURE 3.3. Schematic of the task from Chatham, Frank, and Badre (2014). At the top is a depiction of the rule participants were given. They used a higher-order context (number) to decide which lower-order context (letter or symbol) would be used in a final match decision (white circle indicates the correct response). The order of presentation of the number determines gating demands. When the number comes first, the relevant item, the letter or symbol, can be updated and the irrelevant one ignored. When the number comes last, both items must be input to memory and then a selection made later about which to select for a response. Adapted from Figure 3 in Badre, D., and Nee, D. E. (2018). Frontal cortex and the hierarchical control of behavior. Trends in Cognitive Science 22(2), 170–188.

Importantly, however, they don’t have to use this input gating policy when the context comes first. Someone might just hold all the items they see in working memory, anyway. Then, they wait for the test to choose the relevant one from within memory and make a response, in effect, using an output gating policy. Given the higher load on memory, this output gating policy would result in a cost in time and accuracy on the test, and so it is inefficient. But it is a feasible way to do this task and would still follow the basic rules.

Thus, we see here in a simplified laboratory example the basic distinction between knowing a rule and implementing it. Our volunteers’ knowledge of the rule itself, in terms of how they should respond given the numbers and letters they see, stays the same regardless of the gating policy they choose. But they can use at least two gating policies to implement this same rule when the number context comes first.

This is an intentionally simple experimental example that shows us how different gating policies can be used to solve the same task. In the complexity of the real world, gating policies are much more complex and harder for us to identify, but the point is the same. We must translate our rules and goals, from making coffee to navigating an airport, into gating policies, and the gating policy we choose will greatly affect the efficiency with which we perform these tasks. That said, in Chris’s experiment, most people do spontaneously use the more efficient input gating policy. Even though no one tells them to do so, they try to match the best gating policy to the problem. And, being humans with efficient cognitive control systems, they settle on good policies, in general. But, they don’t have to do so, and under the right conditions they will adopt the less efficient policy, at least initially.

This was demonstrated in an experiment conducted by another scientist, Apoorva Bhandari, working with me.[14] Unlike with the context-first condition, when the context comes last, people must use the output gating strategy. Since they don’t know whether the symbol or letter is relevant, they will have to remember both and then choose one from memory at the end, when they see the context. Apoorva asked what would happen if people had only ever experienced the context-last orderings and then were given a series where the context appeared first. Would they immediately switch and start using the input gating policy? Or, alternatively, would they stick with the output gating policy that they had learned to use throughout their experience with this task?

To answer these questions, Apoorva gave people the same rule and task that Chris Chatham had administered, with one key difference. When volunteers first performed the task, they got only the ordering with context appearing last and so used only the output gating policy. Then, after they had practiced this order many times, Apoorva switched to giving them series in which the context appeared first. This was the first time the subjects had encountered this ordering. And, as expected, they were slower to choose the correct target after the change. In fact, they were even slower than people doing the task with this ordering for the very first time.

As it’s important, I’ll unpack the significance of this last observation a bit more. People are always slower when they first do a task; this at least partly reflects the time they spend identifying the right gating policies. The present task is no exception. When people with no experience with the task first try a series with the context appearing first, they are slower to respond compared with when they have some practice. What is striking here, however, is that Apoorva’s volunteers did have experience with the task, but only under the context-last conditions that require an output gating policy. As a result, when they were put in a situation in which they should use an input gating policy, they were even slower to respond than people who were doing the experiment for the very first time. Apoorva showed in subsequent control experiments that this slowing was because they were using the output gating policy rather than the input gating policy. They could do the task, but inefficiently, because they had chosen the wrong gating policy; they had poorly mapped the rule to their action.

This experiment provides evidence of negative transfer of a gating policy. Recall that transfer is bringing a solution from one problem to another, analogous problem. With negative transfer, doing so makes one systematically worse at the new problem. People’s experience with the task under discussion led them to adopt an output gating policy that works when the context appears last but is inefficient when the context appears first.

What does this mean in the real world, when we aren’t remembering just letters and wingding characters? I’d argue that the implications are profound. When we implement a plan in a new environment, we need to get our working memory gating policy right. With a little experience, we typically come around to good gating policies, and we do so pretty quickly. Transfer from previous analogous experiences is an important piece of this process, and the gating policy we use will determine how effective we are at the new task. In other words, we build this information about the gating policy right into our concept of how to do the task, and we can readily access it when doing the task again. It is part of our compositional representation of the task, and so we can take gating policies we associate with one task and use them in new tasks as the situation demands.

It follows that we must identify and execute the right working memory gating policies given our goals. Choose a good policy and you will perform the task well; choose a bad policy, and your performance may be inefficient, error prone, or fail entirely. These policies can be complex, but at their base, they simply are specifying what information to let into working memory and what information in memory to let out and use as a contextual control signal. Reframed in this way, our question is no longer how does the brain enact cognitive control but, rather, how does the brain learn and implement a working memory gating policy? It turns out that the system the brain uses for working memory gating is not unique to working memory. Rather, the working memory gate may have evolved from a more basic and ancient circuit that controls simple movement functions.

The Motor Gating Circuit

When you make a voluntary movement, whether with an arm, a leg, or a finger, the command to your muscles to do so is issued by your primary motor cortex (see Figure 1.1 in chapter 1). Neurons in primary motor cortex project to the spinal cord along a major axonal highway called the corticospinal tract. In the spinal cord, motor cortex neurons connect directly or indirectly through interneurons to spinal motor neurons that innervate the skeletal muscles. In this way, particular muscles or muscle groups are controlled by primary motor cortex neurons along that strip of motor cortex.

Just in front of the primary motor cortex is a set of neocortical zones referred to as the premotor cortex and supplementary motor area. These two areas are among the major sources of input to the primary motor cortex. In contrast with motor cortex, which commands immediate, voluntary movements, the supplementary motor and premotor cortex are important for movement planning. Much like neurons of primary motor cortex, premotor neurons also come to fire for particular movements, but firing in these cells can be elicited even when a person is only considering the movement rather than executing it.

Further, cells in premotor cortex can fire for mappings between a sensory input and a specific response—in essence, a simple rule. For example, in the laboratory, a monkey might learn that when a particular image appears, say a picture of a balloon, it should move a joystick to the left to receive a rewarding squirt of juice. This rule is an arbitrary mapping between a stimulus and response, because there is nothing particular about balloons related to leftward joystick movements or juice squirts outside the context of this experiment. Despite the rule’s arbitrariness, however, the animal can learn it and will readily move the joystick to the left upon presentation of the balloon.

Notably, once the rule is learned, specific cells within the premotor cortex will come to fire in response to the presentation of balloon on those trials when the animal plans its leftward movement. Indeed, like cells of the prefrontal cortex, premotor cells can come to fire for the rule itself—in this case the image of the balloon and the movement—rather than for either just the stimulus or just the response without the two being linked.[15] These simple stimulus-response rules are sometimes termed first-order policies in that they directly instruct how to behave based on a stimulus, in this case a balloon.[16]

This ability of premotor cortex to represent simple rules linking stimuli with movements distinguishes it from primary motor cortex, and is likely a consequence of the information it receives. Unlike motor cortex, which gets much of its input from the premotor cortex, the premotor cortex gets its input from other neocortical areas of the brain, such as parietal and occipital cortex, which process information from the senses into perceptions of objects, images, locations, and so forth. Thus, premotor cortex is well positioned to take in basic information about the world and generate possible movement plans based on those inputs, in accord with its rudimentary rules. Those rules can then influence motor cortex, where the movements they specify are elicited.

Importantly, however, the activity among premotor neurons is typically not of sufficient strength to cause motor cortex to elicit an actual movement. Providing that drive requires interaction with a structure called the thalamus and the involvement of yet another set of structures, termed the basal ganglia.

The names and relationships among the nuclei of the basal ganglia are notoriously convoluted and arcane, and they surely compete for a top place among the most dreaded sources of undergraduate neuroscience exam questions. Yet, from these knotty relationships comes the brain’s version of a gate. This next bit is going to get into a little more hard-boiled neuroscience, but nature has come up with an elegant solution for gating of a movement. Let’s peek under the hood to see how this gate works.[17]

Premotor and primary motor neurons share mutual connections with the thalamus, as illustrated in Figure 3.4. Rhythmic changes in neural activity between the thalamus and cortex provide the drive required to generate a specific movement. However, under most circumstances, this thalamic drive is absent, because the thalamus is being constantly suppressed or inhibited by a nucleus of the basal ganglia called the internal segment of the globus pallidus. Metaphorically, a gate is closed preventing the corticothalamic drive needed to move. Thus, to actually produce the movement being considered by premotor cortex, it is necessary to inhibit the globus pallidus and so remove its suppressive influence on the thalamus. Inhibition of inhibition results in excitation, like multiplying two negative numbers. Scientists call this neural double negative, disinhibition.

Two populations of neurons in the striatum send input to the globus pallidus and thereby control the gate. Given their function, these two populations of neurons are sometimes referred to as “Go” and “NoGo” cells.

Let’s start with Go cells. When Go cells fire, they inhibit the globus pallidus. This suppresses the globus pallidus, which in turn disinhibits the thalamus. The resulting increase in thalamocortical drive results in the elicitation of whatever movement is being considered by premotor and motor cortex. Hence, when the Go cells fire, the metaphorical gate goes from closed to open, and the movement proceeds.

While the firing of Go cells makes a movement more likely to be executed, firing of NoGo cells has the opposite effect. NoGo cells project to a different area of the globus pallidus called the external segment. The external segment of the globus pallidus exerts yet another inhibitory influence, this time on the internal segment. NoGo cells inhibit this external segment, disinhibiting the internal segment, which allows the internal segment to exert an even greater inhibitory influence on thalamic drive. In other words, the NoGo cells pull the gate closed more tightly and make the movement less likely to occur.

FIGURE 3.4. The corticostriatal feedback loop. (A) A coronal slice through an MRI image of the frontal lobe shows the internal structures of the basal ganglia. Labeled areas are the caudate nucleus (CN), putamen, globus pallidus internal segment (GPi), and external segment (GPe). (B) This schematic shows the functional anatomy of the skeletomotor loop. Inhibitory inputs have rounded ends. The Go cells from the putamen are indicated with a solid gray line, and the NoGo cells with a dashed gray line.

It can be hard to follow all those inhibitions without a pen and paper, but the main thing to remember is that a movement being considered by the premotor cortex is less likely to be produced when the NoGo pathway gets involved, and more likely to be produced when the Go pathway is involved. Thus, the Go and NoGo pathways compete to open or close the gate to a movement, respectively.

What influences the Go and NoGo cells? First, there are broad inputs to the striatum from all over the neocortex, including areas of the brain that process perceptual information from the senses. Thus, the striatum, much like the premotor cortex, has access to the sights and sounds that provide a context for action. If the premotor cortex is hearing about any juice-predicting balloons, then so is the striatum.

Second, striatum receives projections directly from the very premotor and motor cortex areas it controls with its gate, so the striatum also hears about the actions currently under consideration by premotor cortex. This arrangement forms a feedback control loop between cortex and striatum.[18] In essence, the Go and NoGo cells decide whether an action considered by the premotor cortex should be taken or not, given all current inputs. Go cells are considering evidence among the inputs in favor of taking the action in premotor cortex. NoGo cells are considering the evidence against taking the action. Thus, the combined firing of Go and NoGo cells is like votes for or against the action considered in premotor cortex. When Go cells win the election, the gate is opened and the action is executed. NoGo cells vote against the action and so increase the number of Go votes required to open the gate. In this way, the brain has a biological servomechanism for controlling when and under what conditions action execution can occur.

At this point, you might be wondering how the Go and NoGo corticostriatal pathways know which actions are good ones to take and which ones are not. This is important, because without an explanation we have effectively posited another homunculus and named it striatum. Learning is again the answer to our homunculus problem, though rather than back propagation, this time reward-based reinforcement learning can replace our homunculus.

As we will discuss in more detail in chapter 7, the striatum also receives a major input from systems carrying dopamine, a neurotransmitter modulated by rewarding experiences. To keep the discussion simple at present, when we take actions that lead to positive or negative outcomes, dopamine levels are modulated upward or downward accordingly.[19] Among other effects, changes in dopamine levels affect the strength of the synapses in the striatum, making Go or NoGo cells stronger.[20] In this way, the striatum can learn over time what combinations of context and action are likely to yield rewarding outcomes in the future, and which are not. Thus, the Go and NoGo pathways use a history of reward to act as positive and negative feedback loops, respectively, for the actions being considered by premotor cortex.

An important consequence of this basal ganglia motor gate is that the brain can consider actions without actually performing them. This is an adaptive ability, indeed. Consider how difficult it would be to survive if you just did every action you thought of. Our short, chaotic lives would be a continuous chain of spilled drinks, stubbed toes, and awkward moments. Gating allows an action to be planned in detail and then to sit in wait, potentiated and ready for the right conditions to act. In this way, execution of actions can be timed to those conditions and circumstances that will place the animal at its greatest advantage, when it is most likely to attain a positive outcome.

Indeed, motor gating is so adaptive it is an ancient and highly conserved biological innovation. The main structures of the basal ganglia and their role in gating the output from the motor system were present in an ancestor that we share in common with the lamprey.[21] The lamprey is an eel-like creature whose lineage split from that of other vertebrates some 40 million years ago! When nature finds a good solution, it sticks with it. And this is a solution shared not just among all primates or even all mammals but among all vertebrates.

However, as mammals evolved neocortex, and primates developed expanded frontal and prefrontal cortex, multiple loops between the cortex and the basal ganglia developed.[22] With these elaborations, the basal ganglia system evolved to serve a new purpose: controlling working memory gates.

From Movements to Memories

Computational cognitive neuroscientists Michael Frank and Randy O’Reilly first proposed the hypothesis that the same corticobasal ganglia loops that control movements might also serve to gate working memory.[23] Michael Frank is now a leading computational neuroscientist and colleague of mine at Brown. However, this idea initially came out of a final project for a class that Frank was taking with O’Reilly, when Frank was a graduate student at the University of Colorado.

Frank had been trying to figure out how to get working memory gating into Cohen’s CDM model. Most of the tricks he tried involved what we might term global gating signals, like bursts of dopamine to the prefrontal cortex that would trigger an update to working memory.[24] The problem, however, was that these global gating signals either updated everything in working memory or nothing at all. This was a clunky way to do business. If there was a task that required hanging on to some information in memory while updating other information, it was not possible using this global mechanism. Likewise, there was no way to control both input and output of working memory with separate gates.

Frank and O’Reilly’s solution was to apply the corticostriatal loop motor gate to working memory in prefrontal cortex. It was an elegant approach. First, it solved the stability-flexibility problem by separating the actions of updating working memory and maintaining working memory into different structures. If the prefrontal cortex supported stability in terms of the maintenance of rules and task contexts over time, the basal ganglia could support flexibility, by enacting input and output gates.

Second, corticostriatal gating got around the problem of global gates, because the loops between cortex and striatum are topographically organized systems. This means that specific sets of cells in the cortex input to a specific sets of cells in the striatum, which ultimately affect the corticothalamic drive of the specific cells back in the cortex. Just as these topographically organized loops can amplify specific movements, they can also select specific items from working memory to update or output. Thus, a corticostriatal mechanism permitted selective, rather than global, working memory gating.

Finally, given the association of the striatum with dopamine, corticostriatal loops provided a very natural way for working memory gating policies to be learned and implemented without recourse to a homunculus. As introduced in the preceding section, the influence of dopamine in striatum could train the Go and NoGo cells in the striatum to determine which task demand information was valuable to update to working memory or to output from working memory, given the task context.

To see how gating a memory might work in this system, let’s step through the basic process. Imagine a context is being maintained by the prefrontal cortex, such as the color-naming task demand during Stroop. The neurons that code the color-naming task send projections to the Go and NoGo cells of the striatum. These two cell populations weigh this input, along with other information about the broader context, and vote for or against acting on it. If the Go cells win the vote, then the thalamus is disinhibited, and there is a transfer of this context information to output layers of the cortex, where its output can serve to strengthen the color-naming response in its competition with word reading. This is exactly analogous to the way that movements were output through the corticobasal ganglia loop. However, here a rule, plan, goal, or context is selected out of working memory to provide a top-down influence. In other words, we have a biological mechanism for output gating.

This mechanism can implement an input gate, as well. As inputs come in to prefrontal cortex from sensory or memory or planning systems they are evaluated for their value by the voting of Go and NoGo cells. In other words, are they worth maintaining in working memory given the current task or context? If they are valuable to maintain, the activity of Go cells and resulting disinhibition of the thalamus triggers the necessary dynamics to maintain this information active in working memory. We have an input gate.

Thus, rather than assuming that we sprouted an entirely new system for doing working memory gating, the brain extended a solution it already had from the ancient motor loop. This fits well with what we know from comparative anatomy of the frontal lobes themselves. As discussed in chapter 2, the primate lineage saw a progressive expansion of the frontal cortex, particularly in the anterior portions of prefrontal cortex. These elaborations reproduced the cortico-striatal-thalamic loops present in older motor areas but now applied the same gating computation to the more abstract kinds of information about tasks and goals and rules coded by prefrontal cells. Thus, the ability to gate memory in more elaborate ways enhanced the control capability of primate and, ultimately, human working memory. This elaboration supported a deepened capacity to link rules, goals, and plans to compositional actions. They enabled cognitive control.

From this perspective, then, it wasn’t just working memory capacity that deepened over the course of evolution, peaking in Homo sapiens; rather, it was the progressive ability to engage more elaborated gating policies. Indeed, as we will discuss in chapter 4, multiple working memory gating loops permit the system to solve deeper planning and task control hierarchies. This deep capacity for gating working memory, when combined with an advanced capacity for episodic future thought, could be the combination needed for human-level general intelligence.

In sum, then, disinhibitory dynamics between the frontal cortex, basal ganglia, and thalamus provide a plausible mechanism for working memory gating. It should be pointed out that this model of working memory gating is far from settled science. Other plausible biological implementations of gates do not require these corticostriatal interactions.[25] Nonetheless, the basic striatal gating hypothesis has received initial empirical support. Neuroimaging experiments have shown that prefrontal cortex and the striatum are active during input gating to working memory.[26] Patients with lesions to the prefrontal cortex versus the striatum, respectively, show problems maintaining items in working memory versus input gating.[27] Taking drugs that increase or decrease striatal dopamine accordingly affects input gating.[28] And output gating increases communication between the striatum and the prefrontal cortex in the human brain, even when no movement is being planned or executed.[29] These observations are all consistent with the hypothesis that working memory gating is supported by interactions between the prefrontal cortex and the striatum. Further, the corticostriatal gating hypothesis not only explains working memory gating but also easily accommodates the learning problem. For this reason, this is the model we will rely on in the remaining chapters of this book.

Let us return to our problem of receiving a text while driving, now armed with some of the basic theoretical ideas we have discussed in this chapter. To avoid reading our text message and selecting a different course of action, it is important that the context of driving is being maintained in working memory; that is, the action of Go cells will need to engage an input gate so this context is maintained in working memory, likely as soon as we are in the car. Furthermore, this context will need to be output gated from working memory at the right moment to influence decisions about whether to focus on the road or the phone.

Even in this cartoon description, it is easy to see that there are multiple places where things could go awry. For example, we could fail to update the context of driving. Or we might lose it from working memory. We could fail to output gate the context from memory or could do so at the wrong time. Further, other control demands in this scenario we have not touched on yet are surely important. For example, there likely are inhibitory mechanisms needed to quickly stop any impulse to check the phone. NoGo cells might partly contribute to this step, making it harder to select the phone action. But, as we will see, this is not the whole story with inhibition. Further, there are motivational dimensions to this problem that might also explain why people so readily fail in this case. In other words, people might look at the phone because they really want to see that text, more than they want to avoid distracted driving.

In the following chapters, we will anchor our discussion of cognitive control within the framework of working memory gating. We will consider the many facets of our controlled behavior from this perspective. And we will add new pieces to this system along the way, ranging from more complex hierarchical control, to inhibition, to motivation, to the control of memory. However, at its core, our prevailing thesis will be that cognitive control requires translating contexts and predictions from our internal model of the world to a set of policies for gating working memory.

CHAPTER 4

Hierarchies in the Head

Like many academics, I travel fairly regularly. As a result, I am keenly aware of the many ways that travel taxes a cognitive control system needed to get things done in unfamiliar circumstances. For example, on just about every trip I take, I must confront the problem of locating a decent cup of coffee first thing in the morning in a hotel, without having to go to any great lengths. For caffeine addicts, you see, coffee itself is required to go to any great lengths, so this is quite a dilemma. And those freeze-dried packets of mummified coffee that many hotels offer in the room are decidedly not a solution.

Finding coffee usually means leaving the room. I might find real coffee in the lobby, or I might find it two blocks away at a coffee shop. However, if I leave the room, I need to get dressed first. Should I fully shower and dress for the day? Or perhaps I should just throw on some jeans, a coat over my pajamas, and a hat over my bed head and try to slip by without talking to anyone. One enterprising cognitive neuroscientist friend of mine from University of California, Davis, has taken to roasting and grinding his own beans and then packaging them in filter packets compatible with the machines hotels offer in the room. The Coffee Problem inspires great and desperate measures, indeed.

When solving the Coffee Problem or any such planning problem, we benefit from the abstract, hierarchical nature of action we introduced in chapter 1. On the first morning of my stay in a hotel, I often do not know exactly how I will get coffee. Rather, my goal of having coffee will hang around without any specifics until I get down to the lobby and see whether they have a coffee shop or a complimentary beverage service or neither. In this form, my goal is an abstraction over all the ways I could have coffee. But even in this abstract form, my goal of having coffee is useful for control. I can plan other activities around it, such as whether I will shower first or not. And I can do all this without knowing the specifics of how I will get the coffee.

Of course, to actually get coffee, I will ultimately have to perform a series of more specific subgoals that I come up with on the fly. For example, if I enter the lobby and see a coffee shop, then I will need to initiate a sequence of queuing, ordering, paying, waiting, getting, and shuffling back up to my room quickly, so no one sees that I’m not wearing socks. I invent these subgoals in that moment and perform them in a particular sequence, only some of which can be conducted out of order. For example, I doubt it will make much difference if I thank the barista before or after I pay. However, I am fairly certain it will make a mess of matters if I try to pay before ordering. Thus, the details of getting coffee include not only the subtasks but also some structure in time by which those subtasks are pursued.

The hierarchical nature of action allows that I can break down my subgoals into increasingly finer sub-subgoals and sub-sub-subgoals until I ultimately arrive at a specific sequence of movements across my hands, fingers, arms, feet, legs, neck, mouth, tongue, and eyes that obtain a particular cup of coffee on a particular occasion. At each finer grain of description, the goals are more concrete and more specific, and usually prevail for a shorter period of time. Further, as they become more concrete, the subgoals I set tend to be more constrained by the particular features of the environment. For example, where I walk while getting coffee is determined as much by the location of the barista, the cashier, and the cream and sugar as it is by my mental subgoal.

Control of any behavior must contend with this hierarchical property of action in some way. Our human control system is adept at organizing our actions not just in the immediacy of the moment but over time and at multiple levels of abstraction. It can hang on to abstract goals, like finding some coffee, while also addressing more immediate concerns, like finding the right method of payment at the register. In this chapter, we will address more directly how our brain does this and controls complex, sequential, and hierarchical behaviors. In particular, we will see how elaboration of the basic working memory gating system introduced in chapter 3 can solve this problem.

It is important to caution, however, that though tasks can be described hierarchically, in terms of goals and subgoals, this alone does not require that the system carrying out a task be itself hierarchical or operate hierarchically. In other words, the brain need not represent actions hierarchically or implement control systems that contend directly with multiple levels of hierarchical structure. The spider from chapter 2 does not maintain multiple abstract goals concerning its web-making plan, though the process of web construction is readily described hierarchically. Thus, simply because an action can be described at multiple levels of abstraction does not mean that the brain is maintaining all these goals and subgoals in the head. Indeed, early psychological theories made some mileage on sequential behavior while assuming that the system was expressly not hierarchical. It is helpful, then, to consider the evolution of these theories, and the various responses to them, to appreciate where action hierarchies fit with cognitive control and how our brain contends with them.

Hierarchical Structure and Behavior

Edward Titchener was among the founders of the structuralist school of psychology in the late nineteenth century. Structuralists tried to understand how simple elements of the mind, essentially mental images, were associated with each other or “structured” to yield psychological experience. Their primary methodology was called introspection. Structuralists sought to use the “mind’s eye” as an apparatus for scientific observation to record the progress of their own internal thoughts.

Titchener believed that sequential thoughts, behaviors, and meanings were formed through a successive series of images connected to each other through chains of associations.[1] When one image came to mind, such as putting sugar in coffee, then the next image, putting cream, would become active because of its association with the first image. Chaining these associated images from one to the next could yield streams of thought that unfolded in time. Sequential behavior progressed by following whatever these images instructed.

To illustrate, Titchener provided the example of giving a speech. In Titchener’s view, when an orator spoke, a series of auditory images would come to her or his mind. Each auditory image would trigger the next image through an association, and this chain of auditory images would guide the speaker along. Titchener could “hear my own voice speaking just ahead of me,” and this instructed him what to say next.

In the structuralist’s associative image chain, then, there is no need for a plan or hierarchical representation of thought or action. The mere association of each step of a sequence with the next in memory is sufficient to guide you through the course of a lecture, a task, or perhaps the entire day, moving from each impending action to the next as the chain of images unfolds before you like a path through the mind.

The behaviorist school of psychology that followed in the early twentieth century rejected structuralism and its introspective methods as unscientific piffle. However, when it came to sequential behavior, they didn’t do away with chaining. They merely replaced chains of associated images with chains of their own: peripheral action chains.

The scientist Margaret Floy Washburn likely did the most to develop the behaviorist theory of sequential action. Washburn started as a student of Titchener’s at Cornell University and, in 1894, became the first woman to be awarded a PhD in psychology. Her career spanned more than thirty years, during which time she transitioned from structuralism to behaviorism and made a number of important contributions to the field. Among her various honors, Washburn was elected president of the American Psychological Association, and she became the first woman psychologist and second woman in any science to be elected to the US National Academy of Sciences, in 1931.

Washburn sought to move from Titchener’s image association chains to a framework that did not depend on conscious experience, which she viewed as an unimportant byproduct of sequential behavior, rather than a cause of it. She replaced image associations with peripheral trigger chains.[2] These chains explained sequential action as a series of reflexive triggers, with the execution of each action being elicited by the bodily sensations of the action that preceded it. Thus, the movements associated with the action of grinding coffee beans elicited sensations that would trigger the associated action of filling the coffee filter. The sensations of filling the filter triggered the action of adding water to the drip machine, and so forth. Other behaviorists, like John B. Watson, extended this idea of peripheral chaining to speech and then thought itself, which he conceptualized as the chaining of subvocal speech.[3]

Whether they depended on central mental images or peripheral action sensations, chaining theories explained sequential behavior without the system having any hierarchical or superordinate goal, much less a control system that influenced the course of action. However, as we shall see, these ideas were a poor match to how people actually engage in sequential behavior.

The 1948 Hixon Symposium at the California Institute of Technology turned out to be a landmark in the science of the mind and brain. The symposium brought together a distinguished group of psychologists, neuroscientists, and mathematicians to discuss the neural mechanisms of behavior from multiple perspectives. Its pioneering interdisciplinary approach likely foreshadowed the field of cognitive science, which would revolutionize psychology only a few years later.

Among the attendees at the meeting was Harvard psychologist and neuroscientist Karl Lashley, who delivered a seminal lecture that focused specifically on how we plan, sequence, and carry out a sequence of actions.[4] In his lecture, Lashley broke sharply from chaining theories and arguably set the stage for the next half century of research in this area by arguing instead for a central, hierarchical control system.

Lashley explicitly criticized chaining theories for excluding a central guiding goal or knowledge of the overall sequence. Poking fun at Titchener’s introspections regarding associated auditory images, Lashley remarked: “What solution of the lecture problem for the lazy man! He need not think but only listen to his own inner voice; to the chain of associated auditory images.”[5] Behaviorists and their peripheral chain theory of language had it even better. “[A behaviorist colleague] had reached a stage where he could arise before an audience, turn his mouth loose, and go to sleep,” Lashley joked. “This clearly demonstrates the superiority of behavioristic over introspective psychology. The behaviorist does not even have to listen to his own inner voice.”

The problem with these sequential models, according to Lashley, was that they could not account for the simple fact that people will finish sequential tasks even when they fail at a subgoal. Have you ever accidently washed your hair twice while showering, or forgotten to grab your wallet when leaving the house? When you committed these errors, did you stop showering at that point or freeze as you left the house unable to go further? Probably not. Rather, you’ve likely had the opposite experience on occasion—not realizing your error until later, perhaps even long after the task was done.

In the course of any task, people will occasionally leave out steps, double a step, reverse a step with another, perform a step at the wrong point in a sequence, or even include a step from another task. Certainly, I’ll pwn that i have makde any nybmer of typpos while wrting tihs bokk. Typos are a sequential error, and they exhibit the same kinds of reversals, omissions, and repetitions seen in more complex sequential actions like showering or making coffee. Yet, when we make typos, we typically don’t terminate the word when the error is committed; rather, we often finish the word and then go back and correct it. Other times, we miss that we made the typo at all, and continue to finish the sentence, paragraph, or entire paper. We may remain unaware of the error until its discovery by a sharp-eyed reader long after.

This slip blindness occurs with more complex actions, too. It is well documented in error-diary studies, in which volunteers agree to record all their various daily follies in a journal for scientists to analyze. These accounts include striking examples of slips that aren’t caught immediately. For example, while having breakfast, one diarist failed to realize they had poured orange juice into their coffee cup at breakfast. They proceeded to drink the whole cup, oblivious to their error, until they noticed the telltale rim of juice left at the bottom of the mug when they were done.[6]

Early chaining models of sequential behavior would not predict that finishing these sequences is possible. Rather, as each step depends on the correct execution of the one immediately preceding it, any distortion of the steps in the sequence would disrupt the sequence catastrophically. If I left out the step of pouring water into my drip machine, then there would be no peripheral sensations related to pouring and thus no trigger for the next association in the sequence, no bridge to the next action. The chain would break, and the task would self-terminate. As I have no knowledge of my overall goal or plan for the sequence, I am stuck, unable to proceed and without a basis for getting back on track.

In place of chains, then, Lashley proposed that we must hold a memory for our overall task goal that abstracts over the individual subgoal steps, in the way that the goal of “making coffee” abstracts over the subgoals of “pouring water” or “adding grounds.” He proposed that these central, abstract representations of the task persist regardless of the status of each individual subgoal step. This abstract representation can determine the next subgoal in sequence. If a subgoal is lost or reversed or repeated, this does not affect the overall sequence, because the abstract task goal is enough to retrieve the next subgoal in sequence. Thus, Lashley proposed that we must have some hierarchical representation of action, with abstract goals being separated from more concrete ones.

Lashley’s ideas also explicitly introduced control into the problem of executing a sequential behavior. He pointed out that the human brain is not a static system that plods along from one subgoal state to the next without any idea of where it is going or where it has been. Rather, because the brain and its situation in the world are dynamic, any progress through a task constitutes a route through multiple changing brain states. The problem for completing a serial task, then, concerns how the system can accommodate small changes to its setting and progress and yet maintain its course.

For example, when I make coffee each morning, there are endless little differences I must accommodate. Sometimes, items have moved around the kitchen. Other times, I am interrupted by one of my children. I might be sleepy or sick or feeling happy or frustrated. It is unlikely that any two instances of performing that task are identical, and so the brain states I experience are not identical either. What connects these diverse experiences is an abstract description, a goal of making coffee. Staying on task while experiencing slight differences in the exact experience of each subgoal, even without making errors, is the norm for sequential action.

Lashley’s insight was that we must have a way of linking subtasks we perform to an abstract overall task representation that persists in time despite these specific differences. For Lashley, this required a central representation of the task that was held separately—such as in a distinct neural representation—from the representation of the subgoal actions. However, it turns out that Lashley may have been only partly right on this point. He was right that there should be a way to hang on to some abstract task representation, but this abstract task representation need not always be separate from the individual subgoal steps.

In his reasoning, Lashley failed to distinguish between the control of sequential action and the execution of an automated behavior. As we’ve already noted, complex sequential actions can be carried out automatically, given enough experience with them. And experience with a task will also affect the sequential errors one makes. For example, when I make coffee at home, I fill the carafe with water and then pour it into my drip machine. As I repeatedly fill my carafe to make coffee each morning, I strengthen this response pathway; I automate it. Thus, I form an association between the subtask of filling the carafe with the other subgoals needed to make coffee. The compositional nature of action allows that if I wanted to heat water to make tea one day using my carafe, I could transfer my carafe-filling subroutine to this new task. However, because of the association I’ve formed between carafe filling and coffee making, there is a chance I might find myself trying to fill my coffee machine with ground coffee beans and absentmindedly making coffee rather than heating water for tea. This type of slip of action is described as a “capture error,” because it is as though the course of action for tea making was captured by the task of coffee making.

On first blush, capture errors sound like a return to a chaining model of sequential behavior. The chained association that carafe filling has with the subsequent subtask of filling my drip machine could presumably explain why I perform this incorrect action, despite my overall goal of making tea. How can this occur in the same system that will finish making coffee even after I failed to pour water into the drip machine?

Fifty years after Lashley, computational cognitive neuroscientists Matt Botvinick and David Plaut addressed the question of how associated representations might result in sequence errors, including capture errors, while not requiring a separate task representation.[7] They constructed a neural network model that learned how to make coffee and make tea. In this model, shown in Figure 4.1, coffee making and tea making simply referred to the sequence of activity patterns across the model’s simulated action neurons that corresponded to the correct subtask at the right point in the sequence. Table 4.1 shows an example task sequence for making tea.

The model learned to perform each subtask in sequence for each overall task of coffee or tea making. After the model learned, Botvinick and Plaut added some “noise” to the model by having sets of neurons fire randomly. This noise made the model vulnerable to errors, including omissions or reversals or capture errors. Nevertheless, akin to human sequential behavior, these slips of action by the model did not result in termination of the sequence. Rather, the model would attempt to complete the task despite the slip.

How did this model complete these sequential tasks when it did not have access to a separate abstract representation of the task goal? To answer this question, Botvinick and Plaut analyzed the patterns of neural activity the model used to perform each subtask. They found two important features of these neural patterns that help us understand both the errors the model made and its ability to perform despite these errors.

First, subtasks from the same overall task had similar patterns of neural activity. This similarity is sometimes referred to as a family resemblance. Relatives in the same family look similar, though they are also distinguishable as individuals. Accordingly, the subtasks from a particular task, though distinct from one another, nonetheless were shaded in their pattern of neural activity based on a family resemblance. Further, as subtasks from an overall task shared a family resemblance, it follows that the overall task could be gleaned from the pattern of activity of the subtask. The model wasn’t just representing the subtask “pouring water”; it had a unique pattern for “pouring water while making coffee.” This way, the model kept the memory of the overall task active, even if some of the subgoals were lost or degraded. Unlike in early chaining models, the model kept track of the overall task, but it didn’t need a separate representation of that task to do so.

FIGURE 4.1 The Botvinick and Plaut model of routine sequential behavior. The model architecture consisted of three layers of neurons fully connected to each other. The environment provided input through what was seen (fixated) and what was held. Based on this input, the recurrent internal representations linked to actions that would affect the environment. Adapted from Botvinick and Plaut (2004), Figure 3.

Second, some subgoals used for both making coffee and making tea also shared a family resemblance with each other. Concretely, “pouring water for coffee” shared a family resemblance with “pouring water for tea.” As a consequence, the model would naturally produce capture errors, for much the same reason that it can persist at a task when it loses subgoal steps. The similarity of the pattern for pouring water for tea to the same subtask of coffee making could result in the model’s switching tasks, from tea making to coffee making, a simulated capture error.

This can be confusing, so let’s consider another example. Imagine you are driving to a friend’s house. However, the route you take to your friend’s house is similar to the route you would usually take to your office. There is overlap in the sequence of turns, and somewhere along the way there is a critical intersection where you would usually turn left to go to your office, but today you need to turn right. This intersection is the critical choice point of the two tasks. If you turn the correct way at this junction, there is no longer a danger of committing the capture error for the remainder of the route, because it no longer overlaps the route to your office. If you turn the wrong way, you might not notice until you pull up to your office parking lot. After that choice, the subgoal steps themselves just reinforced persistence on the wrong task.

TABLE 4.1. Example task sequence for making tea according to the model in figure 4.1.

The Tea Task: One of Two Versions

Step

Fixated object

Held object

Action

1

cup, 1-handle, clear-liquid

nothing

fixate-teabag

2

teabag

nothing

pick-up

3

teabag

teabag

fixate-cup

4

cup, 1-handle, clear-liquid

teabag

dip

5

cup, 1-handle, brown-liquid

teabag

fixate-sugar

6

packet, white-paper, untorn

teabag

put-down

7

packet, white-paper, untorn

nothing

pick-up

8

packet, white-paper, untorn

packet, white-paper, untorn

tear-open

9

packet, white-paper, torn

packet, white-paper, torn

fixate-cup

10

cup, 1-handle, brown-liquid

packet, white-paper, torn

pour

11

cup, 1-handle, brown-liquid

packet, white-paper, torn

fixate-spoon

12

spoon

packet, white-paper, torn

put-down

13

spoon

nothing

pick-up

14

spoon

spoon

fixate-cup

15

cup, 1-handle, brown-liquid

spoon

stir

16

cup, 1-handle, brown-liquid

spoon

put-down

17

cup, 1-handle, brown-liquid

nothing

pick-up

18

cup, 1-handle, brown-liquid

cup, 1-handle, brown-liquid

sip

19

cup, 1-handle, brown-liquid

cup, 1-handle, brown-liquid

sip

20

cup, 1-handle, empty

cup, 1-handle, empty

say-done

Source: Reproduced from Botvinick, M., and Plaut, D. C. (2004). Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review 111(2), 395–429, Table 3.

An essential aspect of this example is that the competing task of going to your office is automatic, that you drive that same route to work each day. In other words, because it is automatic, the wrong task has an advantage in the competition, and so it is likely to capture your performance at the critical overlap point. If you have made a soufflé only once in your life, there is little worry that you will suddenly find yourself making one when you crack an egg.

Botvinick and Plaut’s model demonstrates that the kinds of sequential errors with which Lashley was concerned could arise as a consequence of associations, without a separate higher-order representation. A key revision to early chaining models, however, is that the subgoals themselves also include abstract information about the overall task in their neural representation. Thus, performing the components of the task supports the overall memory of the task one is doing. In such situations, a separate representation of the overall task goal is not required to explain how people perform well-learned sequential tasks.

This does not mean, however, that we never have an abstract goal for what we are doing that we hold independently in mind from the subgoals of a task. As the opening example illustrated, I can have the goal of getting coffee long before I am performing the specific subgoals to the get the coffee; indeed, I can have it before I even know what those steps will be. Control of sequential action may be the particular circumstance when these abstract representations are used.

There is now neuroscience evidence that when we control our performance on sequential tasks, we do rely on distinct, abstract representations, just as Lashley hypothesized. These abstract representations are what help us assemble new sequences on the fly out of our precompiled automatic routines, to monitor our performance to avoid capture errors and other slips, and to get us back on track should we make an error.

My colleague Theresa Desrochers in the Department of Neuroscience at Brown is an expert in the neuroscience of sequential behavior. Theresa and I collaborated when she was a postdoc in my lab in order to understand the neural systems supporting sequential cognitive control.[8]

We asked our research volunteers to categorize either the color or the shape of a stimulus presented on a computer screen. There was nothing about the stimulus that told them which task to do. Rather, we instructed them to follow a specific sequence of categorizations as each item appeared. For example, they might be told that for the first item they see they should categorize its color, then shape for the next item, then shape again for the item after that, and finally, color for the item after that. As items continued to appear, they would repeat that four-task sequence—color, shape, shape, color—until items stopped appearing. This is a laboratory version of a sequential task, but instead of subgoals like “pour water” and “grind beans,” here the subgoals were to categorize the color or shape of items. And as there were no cues telling our volunteers which task to do, they had to do so based on their own internal register of where they were in the sequence.

I should note that this situation is not unusual in real life. We often do not have cues in the environment to tell us what task to do next in a sequence. For example, when most people shower they take a top-down approach, washing their hair, then face, then body, and so forth. However, if they zone out for a moment thinking about something else and forget what they have already done, there are few cues to tell then whether they have already washed their hair or face. Yet, people can and do keep track of what they are doing in these settings and perform these kinds of sequential tasks routinely. Our experiment was no exception. People were able to track which task to do in their head and cycled the four-task sequence correctly.

Theresa scanned our volunteers using fMRI while they performed this sequential task, and she discovered that a region in the front-most part of the prefrontal cortex, called the rostrolateral prefrontal cortex, progressively ramped up its activity with each step through the four-task sequence, having its minimum at the beginning of the sequence and reaching its maximum at the end. In other words, activity in the rostrolateral prefrontal cortex tracked the sequence position. Its activity did not change, however, based on what specific categorization task was being performed at that position or what physical response the person was making. The activity in rostrolateral prefrontal cortex was tracking an abstract property of the task, namely, the sequence position, which was separate from the subgoals being performed.

Further, this activity in rostrolateral prefrontal cortex was needed for task performance. In a second set of experiments, Theresa found that when she stimulated the participants’ rostrolateral prefrontal cortex, using a type of safe focal brain stimulation technique called transcranial magnetic stimulation, she could disrupt their behavior and induce errors with increasing likelihood as the sequence progressed. Stimulating other parts of the brain did not have this effect. In other words, the rostrolateral prefrontal cortex is increasingly necessary as a sequence progresses, regardless of the specific subgoals being performed in the sequence.

We will discuss the rostrolateral prefrontal cortex further later in this chapter, but Theresa’s experiment demonstrated that humans track abstract properties of sequential tasks, like sequence position, and use them to aid their performance of the task. Other recent work has used sophisticated neural decoding methods to show that the brain tracks sequence position, abstracted over the specifics of the subtask being performed.[9]

Thus, evidence from the brain indicates that humans can maintain separate, abstract contextual information about sequential tasks, as Lashley anticipated. When we automate tasks, it is likely that we form association chains, as in the Botvinick and Plaut model. These chains allow for stable behavior without much input from cognitive control systems. Indeed, for heavily automated tasks, we often rely on this chaining property. If we are reciting the alphabet and get interrupted, it is often easier to start over than try to pick up from where we left off, for example.

Importantly, however, capture errors and other slips of action also arise because of the associative nature of these chains. We can reduce these errors though cognitive control that relies on separate task-context representations maintained in working memory. However, because the structure of sequential action is hierarchical, this monitoring and control should occur at multiple levels of abstraction, as well. I should be able to monitor and provide control for my coffee task at the level of the subgoals, like pouring water, or the higher-order goals, like making coffee, depending on where the overlap with conflicting tasks arises and where state support is needed.

This ability to exert control at multiple levels of abstraction is the problem of hierarchical cognitive control. How does the brain solve this problem to control complex behavior? In the remaining sections of this chapter, we will delve a bit more into how we can structure tasks hierarchically, and the strengths and weaknesses of doing so. Then, we will consider how the corticobasal ganglia gating mechanisms we introduced in chapter 3 are well suited to managing these kinds of hierarchical structures.

Tasks and Trees

The principles of hierarchical cognitive control during sequential behavior also apply to following rules that link circumstances in our world to our behavior. For example, as we saw in chapter 3, the interference during the Stroop task arises as a result of overlap between two pathways at the response layer. The propagation of neural firing from the sensory to the hidden to the response layer in CDM is like the sequential pathway through subgoals while making coffee or turns while driving to a friend’s house. Just as with sequential tasks, overlap between response pathways results in competition and the risk of “capture” by the stronger word-reading pathway when one should be naming colors. To resolve this competition, a separate, abstract representation of a task demand tips the competition between the pathways in favor of the appropriate task. Thus, following a rule that maps stimuli we perceive to the responses we make shares much in common with a sequential task.

Also, just as with sequential tasks, rules themselves can be expressed at multiple hierarchical levels. For example, when they were younger, both of my boys struggled mightily with the “indoor voice” rule. You might be familiar with this rule. It stipulates than when we are inside, we should speak in a soft voice, whereas outside we are allowed to be loud, both in voice and behavior. For a number of years, reminders to use “our indoor voice” were a constant refrain around our house. No doubt this was partly because this is a challenging control problem for a child. The child must hold the context of being indoors in working memory and then use this to influence how they speak, perhaps in opposition to the overwhelming impulse to shout at their sibling or shriek with joy.

As they got older, however, our boys developed a more sophisticated, deeper hierarchical version of this rule, shown in Figure 4.2A. They worked out that the indoor voice rule is a member of a class of rules that applies only if my wife and I are around. In this new rule, I am a context, but a superordinate and abstract one. My presence doesn’t directly influence how they speak. Rather, my presence tells them how to relate other aspects of their context, such as whether they are indoors or outside, to their behavior. Thus, monitoring superordinate contexts is often necessary to resolve competition about how other aspects of context itself might be used to control behavior.

One can set up a similar hierarchical rule structure to the indoor-voice example, in the laboratory, but using simpler contexts and behaviors. Imagine that you come to my lab. I place you in front of a computer screen, and I give you a button to hold in each hand. Then, I tell you, whenever you see a blue object on the screen, press the button in your left hand. When you see a red shape, press the button in your right hand. These simple rules are depicted as a decision tree in Figure 4.2B. There’s a single branch point on the tree, which specifies how you choose a response based on color.

You get the hang of this simple task pretty rapidly. Then, I throw some more rules at you. When the object is a square, press with your left hand, and if it is a triangle, press with your right hand. Uh-oh, now you have a problem. If you see a blue triangle or a red square, what should you do? The color rules are telling you to press one way, and the shape rules, the other. These simple rules, mapping stimulus to response, are now overlapping; there is competition between the pathways.

You will need to add some additional information to distinguish when to use the color rules and when to use the shape rules. For example, I might instruct you that when the object is small, do the color task, but when it is large, do the shape task. This new rule structure is shown as a decision tree in Figure 4.2C. We have added a hierarchical level to your task structure, making the decision tree deeper. Analogous to the role that an indoor or outdoor context plays in a child’s decision about whether to shout, “size” in this example is an abstract contextual feature. It does not provide a context for how to respond directly. Rather, it instructs how to relate other features of your environment, colors or shapes, to your response.

FIGURE 4.2 Hierarchical rule trees and policy abstraction. (A) The “indoor voice” rule as a second-order policy relating a parent’s presence with whether one should relate an indoor/outdoor context to how one speaks. Adapted from Badre and Nee (2018), Figure IA. In the laboratory, rules can increase policy abstraction by adding levels of contingency to decision trees across (B) first-, (C) second-, and (D) third-order policy.

As this task gets more complex, I can continue to add more contexts and more branch points on my rule tree. For example, say you come back to my lab the next day. Today, I tell you that you should decide whether to do the color or shape task depending on the position of the object on the screen, rather than its size. Specifically, when the object appears on top, do the shape task, and when it appears on the bottom, do the color task.

As shown in Figure 4.2D, the tree has been made deeper by another step. You now have two second-order rule trees: one uses size to determine the rule set, and the other uses location. To keep these separate, you need a new superordinate context that can distinguish which subordinate context—size or location—is relevant right now. In this example, that superordinate context is actually the day or episode when you are doing the task. During this episode, you are using location to choose your task. If I changed back to size as the subordinate context later in the visit, you would need to keep track of that change in episode again.

Well, that escalated quickly. We started with a couple simple stimulus-response rules; these are sometimes termed first-order policies, because they express a direct relationship between a stimulus feature, like the color red, and an action, like press right. But, as new rules were learned, the overlap and competition among policies grew. To reduce this overlap, it was necessary to build increasingly higher-order policies based on new contextual elements. Each time we added a context, the rule tree got deeper with a new branch point. In a couple steps, your task was represented by a third-order policy in which an episode determines whether size or location determines whether color or shape determines the button to press!

Trees like that depicted in Figure 4.2D are an efficient way to represent hard problems. As we add levels to this tree, the number of relationships between states in the world and the behaviors they entail that we can accommodate grows exponentially. This description of these relationships is far more compact and efficient than if we had simply created a list of all those combinations of episode, size, location, shape, color, and response one by one—sometimes called a lookup table. As a decision tree, our lookup table would be a very wide, flat tree with as many branches as we have combinations of features and responses. That is a lot of options to choose from and each branch has to be learned on its own. Instead, if we can behave according to the rule expressed in Figure 4.2D, we are effectively solving this hard problem using only a few decision points. Using hierarchies as a way of structuring behavior has big payoffs.

Given the advantages of a decision tree, humans are able to behave according to rules like those in Figure 4.2D, and even deeper ones, but there are costs to offset the advantages. As these trees get deeper, people have to keep track of more contextual features, and they have to relate them to one another hierarchically. With deeper rule trees, people’s response times gets slower, and they start making more errors. Despite these costs in performance, however, there is considerable evidence that humans leverage the hierarchical structure of a task to control their behavior. Indeed, they will attempt to find these treelike hierarchical rules when they enter a new task.

A few years ago, I conducted an experiment in collaboration with neurologists and cognitive neuroscientists Andy Kayser and Mark D’Esposito that tested people’s ability to discover and apply hierarchical structures in a new environment.[10] The experiment was similar to the hypothetical version I just described, except that we didn’t tell people the rules. Rather, our participants had to figure out the rules based only on feedback they received about whether their responses were correct or not.

In this experiment, people were shown a series of stimulus displays, each of which consisted of a shape appearing at a particular orientation (up, down, or diagonal) and surrounded by a colored box. They were told that, depending on the specific display, pressing one of three buttons on a keypad would earn them a small amount of money. However, it was up to them to figure out the right button to press given the particular display. They would be told after every response whether they had earned money or not. On the basis of this feedback, they could learn the mapping between each combination of color, shape, and orientation and a correct response. Across all the displays, there were three shapes, three orientations, and two colors. So, in total, there were 18 unique combinations of these three features mapping to three possible responses.

Learning which response maps to each of these 18 stimulus displays based only on feedback is a challenging learning problem. However, without telling our participants, we set up a higher-order, hierarchical structure among the mappings. For example, whenever the color in the display was red, then the shape of the object cued the response, and when it was blue, orientation cued the response. Thus, if participants could figure out this hierarchical structure, the learning problem became much easier. Rather than learning all 18 individual rules, they could learn one higher-order rule that mapped color to either shape or orientation. Then, they needed to figure out only the three rules for each of these, and they were set. They had a compact, hierarchical version of the 18 rules.

Importantly, we didn’t tell our participants any of this. Indeed, we even included a control version of the experiment in which no such hierarchical structure existed. In this case, each conjunction of shape, color, and orientation mapped uniquely to one of the responses, so the people would need to learn all 18 unique mappings individually. We called this the “flat” rule, because there was no hierarchical structure.

The instructions, setup, and look and feel of the learning session were kept identical between flat and hierarchical conditions. The only difference was the hidden hierarchical rule structure in one of the conditions. Despite these controls, people were able to discover the hierarchical rule when it was there, and they used it to greatly improve their performance in the hierarchical relative to the flat condition. Moreover, people discovered this hierarchical structure early on in their learning. In the flat condition, by contrast, people learned slowly and were never able to figure out all 18 rules.

In follow-up work with Michael Frank using mathematical modeling, we found that people’s rapid discovery of the hierarchical structure occurred because they were actively looking for this structure from the outset of the experiment.[11] Conversely, it was only after a hierarchical structure failed repeatedly that they tried to learn each conjunction rule on its own. Indeed, in subsequent work by Michael Frank and Anne Collins, people would learn a hierarchical structure, even when it conferred no advantage over a flat structure.[12] So, confronted with an unfamiliar problem, people can rapidly discover and apply a hierarchical rule structure if it is present to be learned.

Why do people look for hierarchical structure, even when there is no particular advantage to doing so? The answer is that people are implicitly investing in the future. Hierarchical structures include abstractions that can be more readily generalized to new tasks. If color was useful as a higher-order context in one setting, it would be easy to transfer this to a new setting where it determined whether location and size was relevant, instead of shape and orientation. Hierarchical structures are also compositional, as each branching of a tree includes a subtree that can be removed from the whole and glued onto the tree structure of a new task. Thus, even if a hierarchical rule is not particularly useful right now, it is likely to be more useful in the future than a flat rule. Indeed, the participants in Anne and Michael’s experiment who used a hierarchical structure were able to readily transfer this rule to a new task. Their bet payed off after all.

To summarize, then, we spontaneously build hierarchical rules to structure our action control in complex environments. These trees are useful because they allow us to keep episodes, rules, and tasks separate that might otherwise compete. They also let us efficiently store rules that solve hard problems. And by adding higher, more abstract contexts to our rule structures, we are able to recombine, swap, and remove lower branches without changing the whole structure of a task; in other words, they are inherently compositional. Thus, hierarchical representations are likely a big piece of the answer to how we readily act to achieve our goals in new, complex environments.[13] As we will see in the next section, working memory gating mechanisms are central to making use of hierarchical structures like these.

Mechanisms of Hierarchical Control

Let’s return to the basic working memory gating framework we introduced in chapter 3, but now using the example of the hierarchical “indoor voice” rule. For my boys to implement the indoor voice rule, they will need to gate the context “indoor” into working memory at the appropriate moment. Then, when the urge strikes one of them to tell his brother something really exciting, he will need to output gate that context to overcome his tendency to shout, tempering his enthusiasm based on his context. These gates can be operated by disinhibitory loops between the cortex, the basal ganglia, and the thalamus that amplify the right context in working memory at the right time.

Some elaboration of this basic framework can also explain the more complex, hierarchical version of this rule. In this version, the context “parent” needs to influence the use of “indoor” as a context, as opposed to influencing speaking directly. However, it turns out there are multiple ways to elaborate our basic control system to solve a problem like this, and each has its own advantages and disadvantages.[14]

For example, one way to do this would be to lump all the contexts together into working memory and use their conjunction for responding. In other words, we would create new contexts from every combination of parents being there or not, and indoor or outdoor. So, you would learn that when you have the conjunction “parent and indoor” in working memory, you speak in a soft voice; if “parent and outdoor,” you can be loud; and so forth.

Figure 4.3A shows what a CDM model might look like that takes this approach. This version has a larger context layer to include all the possible contexts, and each unique conjunction of contexts has its own pathways to a particular response. Wired correctly, this model will follow the more complex rule, but it has some serious disadvantages. You can see from Figure 4.3A that we had to grow the size of the task demand and hidden layers in the original model to accommodate these new pathways. All these added connections will make the whole architecture a bit harder to learn, as each separate pathway needs to be wired properly, so the model needs more experience with every conjunction to link all these new contexts with the right behavior.

Beyond the time this takes, the model is limited to the pathways we have taught it. For example, the model would be unable to benefit from its prior knowledge if we added a new indoor rule like walking rather than running when inside the house. To add this new bit, we would need to map all our various conjunctions of parent and indoor/outdoor to this new rule.

FIGURE 4.3 Schematics of two hypothetical modifications of the CDM model for hierarchical control. To solve a hierarchical task like the indoor/outdoor voice rule when a parent is present or absent, one could (A) map conjunctions of the contexts to various actions. (B) Alternatively, one could nest two CDM controllers into a hierarchical control architecture. Adapted from Badre and Nee (2018), Box 1, Figure I.

This is a cumbersome way to go about doing things. If our brain worked this way, our life would be spent learning every individual conjunction of contexts and the behaviors that follow from them. We would have little ability to generalize or transfer outside of the conjunctions we had learned. This solution would quickly fall afoul of the scaling problem we discussed in chapter 1.

Part of the issue with this solution to hierarchical control is that it has sacrificed the hierarchical structure inherent in the original rule. By relying on conjunctions of contexts, we have effectively flattened the whole rule structure, and so there are no higher- or lower-order contexts or compositional constituents. To take advantage of this structure, we need a way to modify our model so that we preserve the hierarchical structure in the rule.

Figure 4.3B shows an example of such a scheme. The original CDM had a context provide an input to sensory-motor pathways that select a response. So, maintaining the context “indoor rules” would affect the mapping between a stimulus and a response to favor a soft voice. Our modified model adds a new context layer for the presence of a parent that influences whether being inside the house maps to the context “indoor rules” or not. In other words, we used the same structure that CDM did originally, but rather than using a context to select a response pathway, we now also have a context that selects another context. We have simply added a second CDM model nested onto the first one.

Separating our contextual control in this way preserves the hierarchical structure of the task in the scheme in Figure 4.3B. As a result, it can do the kind of transfer and generalization that the toy model in Figure 4.3A cannot. For example, we can add new rules to the class of “indoor rules,” like running versus walking, without having to relearn the relevance of a parent’s presence. Anything attached to the indoor rule just inherits this relationship.

While this scheme has many advantages, it is also clear that this structure adds some serious demands on our working memory gating mechanisms. We now have two context layers, and so if we want to control these separately, we will need more gates associated with each. These gates need to be coordinated with respect to each other to produce the correct response at the right time. Thus, implementing a hierarchical rule places correspondingly high demands on working memory gating.

How are these multiple gates supported in the brain? In our investigation of the hierarchical learning task described previously, Michael Frank and I addressed this problem in a neural network model.[15] Just as in the simpler cases we discussed in chapter 3, hierarchical gating can be supported by the corticobasal ganglia circuit. We have already seen how a cortico-striatal-thalamic loop can implement an output gate for a context maintained in working memory. Activation of a neural representation in cortex of a useful context leads to the Go neurons in the striatum amplifying this neural representation via pallidal disinhibition of the thalamus. In this way, at a particular moment, a context like “indoors” is gated and used to select the response policy “speak softly.”

To add the higher-order context, we add a separate loop that output gates the context “parent.” This nested looping structure is depicted in Figure 4.4. Thus, the prefrontal cortex maintains both the context indoor and the context parent separately from each other. Corresponding separate corticostriatal loops independently learn when to output gate these contexts.

Importantly, these separate loops are nested and interact hierarchically. Specifically, the output from the cortex of the superordinate loop projects to the striatal gate of the subordinate loop. In Figure 4.4, this input is labeled as a diagonal connection. When the superordinate context “parent” is output gated by its striatal gate, it provides a contextual input through the diagonal connection to the Go and NoGo cells considering whether to output gate the subordinate context “indoor.” When they receive that input, they are more likely to vote to output gate the “indoor” context. In this way, a hierarchical rule is implemented by separate, nested corticostriatal gates.

There is growing evidence that this hypothetical nested looping architecture may hit close to the mark in terms of how the brain implements hierarchical rules. In chapter 2 we saw that humans are distinguished from other primates and even other great apes like the chimpanzee by our greatly expanded prefrontal cortex. These expansions appear to follow a roughly back-to-front progression. Anatomists refer to brain structures toward the front as rostral, from the Latin word rostrum, meaning “beak,” and the structures toward the back as caudal, from the Latin word cauda, meaning “tail.” So, these differences from the beak to tail of the frontal lobes are often referred to as the rostrocaudal organization of frontal cortex.

FIGURE 4.4 Hierarchical gating through corticostriatal loops. Independent loops between PFC and striatum are shown. Each loop acts as a disinhibitory positive feedback loop that can gate its portion of cortex. The loops are nested asymmetrically, such that context information outputted from higher-order loops influences the striatal Go and NoGo pathways of the lower-order loops. Callouts illustrate how these loops might solve the indoor/outdoor voice rule.

A number of trends follow this rostrocaudal organization. Neuron density increases toward the front of the brain, and there is higher connectedness among the neurons themselves, potentially reflecting more integrative and abstract neural representations. Further, as introduced in chapter 2, the pattern of connectivity shifts from long-range connections and toward local connections rostrally within the frontal lobe. Thus, the overall organization resembles a hierarchical architecture with rostral regions featuring more integrative cortical representations that exert their influence primarily on other caudal frontal regions rather than directly on posterior cortical networks.

Further, in the last decade, more direct evidence has indicated that this rostrocaudal organization is important as people engage in hierarchical control. Neuroimaging studies in humans from our lab and others have provided consistent evidence that as people perform higher-order rules with deeper decision trees, they engage regions that are further rostral in the lateral prefrontal cortex,[16] as shown in Figure 4.5. Patients with damage to the more rostral areas of the frontal lobe show more performance deficits on deeper rule trees than on shallower ones. Conversely, patients with damage more caudally in frontal cortex exhibit deficits with both shallow and deep trees.[17] In more recent work in healthy volunteers, Derek Nee and Mark D’Esposito showed that stimulating different regions of the prefrontal cortex using transcranial magnetic stimulation also produced a hierarchical pattern of deficits.[18]

In the decade and a half since these initial observations, considerable work to deepen our understanding of how prefrontal cortex is organized to support hierarchical cognitive control has resulted in several important refinements to the idea that deeper hierarchies are controlled by more rostral prefrontal cortex.

First, though there is a qualitative rostrocaudal functional organization that is crucial to hierarchical tasks, there is no single functional gradient of abstraction that encompasses the entire frontal lobe. The term functional gradient implies there is one axis of abstraction that defines the organization of the frontal lobe in step increments. But it is not the case that one can start at the motor cortex with control over the simplest concrete actions and, in a stepwise neuron-by-neuron way, get progressively abstract actions represented in neurons moving forward to the rostral pole of the frontal lobe.

Rather, the different regions identified in fMRI studies are actually members of broader distributed networks between frontal and parietal cortex association areas that are active with increasing hierarchical levels.[19] Indeed, these are subnetworks of the broader frontoparietal control system discussed in chapter 3. As a consequence, though large components of these networks in prefrontal cortex follow a caudal to rostral progression, there are also exceptions to this pattern. Thus, one can better predict how regions will participate in hierarchical control based on their network relationships rather than on their rostro-to-caudal location.

FIGURE 4.5. Lateral view of the frontal lobe showing the hierarchical organization of control subnetworks. The approximate locations of rostrolateral prefrontal cortex (RLPFC), mid-dorsolateral prefrontal cortex (mid-DLPFC), anterior premotor dorsal (prePMd) and ventral (prePMv), and premotor and motor cortex are shown. From premotor caudally to mid-DLPS, roughly more rostral regions have been associated with more abstract and complex hierarchical rules. However, these regions are members of broader subnetworks found in every lobe of the brain. These are shown using textured shading. Adapted from Badre and Nee (2018), Figure 2C.

Second, the top of the hierarchy is not actually the network that includes the most rostral prefrontal area, the rostrolateral prefrontal cortex, which was initially considered to be the top of the frontal lobe hierarchy. It was hypothesized to be the place where the most abstract contexts that prevailed over the longest periods of time were maintained. This idea has proved to be only partially correct.

One clue to the ranking of a particular brain region within a frontal lobe hierarchy is its asymmetric pattern of connection with other regions. Think of hierarchies as they exist elsewhere in the world. In a corporation, for example, the decisions of a CEO greatly affect all employees, but the decisions of the mail room attendant minimally affect the CEO. Superordinate regions, those higher up in the hierarchy, should influence those below them, rather than vice versa. If influence is expressed in a brain as strength of connection, this predicts an asymmetry of greater output rather than input from a brain region higher up in the hierarchy.

Anatomical studies conducted in the monkey brain and studies of functional connectivity in the human brain have used this definition to rank regions in the frontal lobe hierarchy, with outcomes remarkably consistent across species. In moving from caudal to rostral frontal cortex the output-to-input ratio grows, consistent with increasingly higher hierarchical rank. But this pattern reaches its peak not in rostrolateral prefrontal cortex but just behind it in the mid-dorsolateral prefrontal cortex.[20] This is roughly the same region we noted earlier is expanded in humans relative to other species. It also matures latest in development.[21]

If it isn’t the top of the hierarchy, what role does the rostrolateral prefrontal cortex and its associated network play in hierarchical cognitive control? Unlike the anterior dorsolateral prefrontal cortex, rostrolateral prefrontal cortex is a major zone of input to prefrontal cortex. Indeed, the studies cited earlier found that its output-to-input ratio is reversed, such that its degree of input is higher than the output. Its output is primarily to the frontal lobe—specifically, the mid-dorsolateral prefrontal cortex. However, its inputs notably come from the network of areas, including the hippocampus and ventromedial prefrontal cortex, that we associated with our long-term knowledge of the world and episodic future thought in chapter 2. Further, as we saw earlier in the study of sequential control by Theresa Desrochers, rostrolateral prefrontal cortex is active during tasks that require engaging in control based not just on our current situation but on what has happened in the past and what might happen in the future. These are important clues toward the role of this region.

In a review of the literature on the rostrolateral prefrontal cortex, Derek Nee and I hypothesized that the rostrolateral prefrontal cortex may be one critical interface between our systems for episodic future thought and cognitive control.[22] It may provide a way of holding in mind information about our explicit internal model of the world and the hypothetical futures and paths we can take. As such, rostrolateral prefrontal cortex is crucial for bringing our knowledge of the past, present, and future to bear on control systems.

To summarize, then, the first requirement for the hierarchical control architecture seen in Figure 4.3B is evident in the brain. There are separable subnetworks that could support control over different hierarchical levels. They are organized such that the simplest rules are supported by networks including areas like premotor cortex that are caudal in the frontal lobe and are close to motor cortex. More rostral networks, which include the mid-dorsolateral prefrontal cortex, may be important for control involving more abstract, higher-order policies. Thus, these separate cortical networks can process different contexts, like “parent” or “indoor,” for gating by different striatal loops.

Importantly, however, this is only part of our hypothesis. The model also predicts that these different cortical networks should be coordinated by separate, nested striatal gates. There is also evidence for this organization. Anatomical studies in the nonhuman primate have long established a rostrocaudal organization of cortico-striatal-thalamic loops.[23] Further, even within the connections from the DLPFC to striatum, there is a fine-grained, ordered rostrocaudal organization of connections.[24] These organized inputs are a basis for each area of prefrontal cortex to be regulated by its own separate cortico-striatal-thalamic feedback loop. This interpretation is further supported by fMRI studies that observe greater communication between specific segments of the striatum and prefrontal cortex depending on the depth of a rule tree being used during hierarchical control.[25]

Studies of white-matter tracts have also found that not all the terminations follow the general rostrocaudal pattern of connections. These are divergent connections that don’t follow the systematic caudal-to-rostral gradient that most connections do. Interestingly, there is an asymmetry in the pattern of these divergent connections. Inputs originating in rostral frontal regions that project back to caudal striatal areas are more likely to diverge than vice versa. This means that neurons in rostral prefrontal cortex will broadcast their contextual signal to caudal striatum more than the other way around.

This is likely an important anatomical feature for hierarchical gating. Recall that the rostral prefrontal cortex also is maintaining the more abstract contextual information and goals. Thus, the caudal corticostriatal loops are being influenced by the contexts maintained in rostral prefrontal cortex, more than the other way around. When a rostral loop output gates an abstract context, like “parent,” it provides a strong input to the caudal loop that can output gate a lower-order context like “indoor.” These were the diagonal connections in Figure 4.4. The opposite influence does not happen to the same degree.

In summary, when we need to implement a hierarchical rule that involves multiple contexts influencing one another, we can do so by coordinating multiple input and output gates on working memory. Each gate regulates working memory for a task context at a particular level of the tree. When a higher-level context is output, it influences the gating operation by a lower-level gate, and so forth. These gating operations are implemented in the brain by a series of nested cortico-striatal-thalamic loops that coordinate the influence of separate networks on each other, such that more rostral networks tend to influence the gating operation of caudal networks. These interactive gating dynamics allow the network to engage in control based on deep rule trees that solve hard hierarchical problems.

Most of the tasks we do and goals we wish to accomplish are complicated. When I look for coffee at the hotel, it involves lots of different subgoals operating over different timescales or multiple contextual elements that constrain one another in complex ways. As we borrow and generalize, the components of action overlap and interfere with each other and need contextual support to move forward smoothly. The ability to represent problems hierarchically is a powerful solution to this problem. However, implementing these hierarchical rules is a challenge.

To confront the challenge of implementing hierarchical rules, the brain can leverage the working memory gating system discussed in chapter 3. By nesting multiple corticostriatal gating loops ordered back to front in the frontal lobe, the brain can solve gating problems for deeper rule trees, and we can learn and generalize our gating policies to new situations by learning about each gating policy separately. I can update my decision to wait in line at a coffee shop separately from my overall goal of having a cup of coffee. If they are influencing my behavior, each of these levels of abstraction is processed through separate gating loops between the frontal cortex and the striatum.

Thus, a major consequence of the elaboration and growth of the frontal lobes in human evolution may have been an increased capacity for this kind of higher-order gating. As we elaborated the gating system, we could manage increasingly deeper rule trees. As already noted, small increases in our ability to manage a rule tree can yield large gains in the kinds of behavioral problems that can be solved. Thus, small changes in the brain’s ability to gate working memory would have yielded major changes in our capacity to get things done.

CHAPTER 5

The Tao of Multitasking

Eugene O’Neill is arguably America’s most decorated playwright. He is recognized for such contributions to the American literary canon as The Iceman Cometh and A Long Day’s Journey into Night. His plays, with their dark and pessimistic portrayal of early industrial society, earned him four Pulitzer Prizes. He is still the only American playwright to win a Nobel Prize in Literature.

O’Neill was also a big-time multitasker. Throughout his career, he was known to work on two plays at once. As anyone who has created a major work of art knows, this is astounding. I write a lot, but I can’t imagine writing another book at the same time as this one. At its highest levels, art requires focused intensity and immersion in the subject matter. Artists commonly spend many months or even years preparing for one project, so it is remarkable that O’Neill could write two different major works at once, with different characters, plots, themes, and settings, and yet also produce high-quality work.

As we will discuss in this chapter, multitasking is generally a bad thing to do, making us less effective and less efficient. So, how did O’Neill pull this off? Was he a genius with unusual talents? Or perhaps, if he’d properly focused on one play at a time, he would have written even more classic plays and won two Nobel Prizes! These are both plausible explanations. However, another possible account for his skill is hidden in the unusual way he worked.

O’Neill used his Nobel winnings to build a home for himself and his third wife, Carlotta, in Northern California, near the town of Danville.[1] The couple closely directed the design of the house. O’Neill was captivated by Eastern philosophy and incorporated many of its concepts into the design. For example, there are paths and hallways that lead to nowhere in particular. Much of the interior of the home has a dark atmosphere, with colored glass mirrors that enlarge the space without brightening it. Owing to its unique design, locals refer to the home as the “Tao House.”

I had the opportunity to tour the Tao House myself when I lived in Northern California. The house was as peculiar and unique as promised, but the part of the house that stood apart for me was O’Neill’s office. The office featured little of the Spanish or Asian influences found throughout the rest of the home. It was designed to resemble the cabin of a ship, reflecting the portion of O’Neill’s youth spent working as a merchant marine. On two opposite walls of the small room sat two desks. The desks faced away from each other, and each held its own unique collection of photographs, trinkets, and baubles that could be seen only when sitting there. The overall effect was that when sitting at one desk, one was immersed in a wholly unique environment, in terms of sights and feel, relative to the other desk. O’Neill’s practice was to write one play on one of these desks and another play at the other, alternating between them by changing desks and never working on them anywhere else.

As a scientist who studies cognitive control, I was taken by this workspace design. Whether by luck or design, O’Neill had struck upon a strategy that allowed the environment to support his cognitive control system. As we will see in this chapter, many of the problems with multitasking stem from performing many tasks in the same environment. As a consequence of this overlap, we are surrounded by lots of cues to tasks other than the one we are doing, and so balancing stability with flexibility demands that our internal control system keep these tasks separate. To behave efficiently, we must build a Tao House in our head somehow and use our internal system to place our tasks on different mental desks. O’Neill’s strategy for multitasking was to outsource this problem to the world itself. He created separate environments for his tasks, and doing so likely aided his control system in getting more than one thing done.

The contrast of O’Neill’s office with our contemporary world is striking. This is not to say that multitasking itself is new. On the contrary, multitasking has long been a mainstay of modern industrial life. Offices have always been single environments where we do many different tasks. For example, at any given moment in my office, I might be conducting research, writing, teaching, meeting with students, or doing administration. Many of these tasks are done in our office, without a change of scenery from moment to moment as we shift among our many roles.

So, multitasking is certainly not new, yet it is also arguably more pervasive now that at any other time in our history. This follows necessarily from the increased connectedness and availability of tasks in the digital age. In the developed world, computing is by now nearly ubiquitous. A Pew Research Center study estimates that more than three quarters of Americans own a laptop or personal computer, and around 90% of American adults under 50 own a smartphone.[2] This percentage goes up to 96% when considering just those under the age of 30. And whereas access to a personal computer is still largely determined by factors like household income, access to a smartphone is comparable and high across household incomes ranges. At least in the US, the penetration of digital devices in our lives is near total.

In their current design, all computers and smartphones are multitask environments. We can use the same device for video and audio conferencing, email, texting, browsing the Internet, social media, entertainment, as a flashlight, and more. We can arguably do as much or more with our smartphones as the occupants of Hogwarts could do with their wands. And these other tasks are made highly available to us. We can and do shift between tasks on our devices regularly. Furthermore, the advent of push notifications, badges, and alerts has permitted other tasks to interrupt whatever we are doing and try to actively grab our attention!

At best, these interruptions distract us. At worst, they induce us to multitask. A 2012 study that collected usage data from more than 3,600 Android users around the world found that around 10% of total usage time was spent on interruptions.[3] This included both external sources, like texts and pushes, and voluntary interruptions by the user, such as pausing reading email to check social media. As these sources of interruption have grown since 2012, this is likely an underestimate of the time spent on interruptions today.

Moreover, our office is a multitask environment, but it is one we can’t take with us. A smartphone is a multitask environment that can live in our pocket. Indeed, a 2013 study by Harris Interactive found that 72% of US adults keep their mobile device within 5 feet of them most of the time.[4] So, our devices come with us, and with them come their complete offering of other tasks to entice, interrupt, and distract us from whatever else we are doing.

It follows that digital media devices are a near-constant source of multitasking in any situation. We have already discussed the prevalent and dangerous habit of texting while driving. Similar examples exist in almost every other area of human activity. The same 2013 Harris Interactive study just cited found that while 55% of respondents said they use their smartphone while driving, 35% use it during a dinner date, 32% at a child’s school function, 20% in church, and 12% in the shower, somehow. People also frequently multitask by using multiple forms of media at once, termed media multitasking. A 2016 Nielsen survey indicated that close to 50% of respondents use a smartphone while also watching television at least once per day.[5]

Teaching spaces are no better. When I give lectures at Brown, I look out at a sea of open laptops behind which students sit, staring at their phone. Students do this despite lots of evidence that taking notes on a laptop is worse for learning and retention than taking notes by hand. This is the case even when other media are not distracting the note taker. But, of course, students are often distracted by the other tasks on their phone while they take notes ineffectively on the laptop.

Thus, people are multitasking with a high frequency, and not just the younger generations. Both the so-called millennial generation and Generation Z, which is now in college, take a lot of undeserved heat, in my opinion. However, one presumed skill these younger generations are positively credited with is multitasking. Some will declare that these generations are “digital natives” who grew up with laptops and smartphones and so are better equipped to multitask in our fast-paced digital world, but these intuitions have not been borne out by data. Younger generations are as bad as everyone else at multitasking, and though they do media multitask a lot, they are not even the generation that does this the most. The winner of that honor is my own generation, Generation X.

That 2016 Nielsen study found that Gen-Xers spend more time on media in general and social media in particular than other age groups, close to around seven hours total per week. For perspective, that amounts to more than three weeks of our waking hours each year spent just on social media. This might make some sense if one considers my generation’s particular stage of life. Those of us between 29 and 50 tend to be further along in our careers than younger generations, and so we tend to have broader professional commitments. In our personal lives, we are statistically more likely to be in long-term, committed relationships and have both younger children and elderly parents. So, as a group, we tend to have more social obligations, through both work and family. These connections pull at us both externally and internally, and so maybe it follows that we are more likely to be the ones constantly checking our phones. Regardless of the reason, however, multitasking is not the domain of the young. And, from an evolutionary standpoint, we are all digital immigrants, dealing with an unprecedented availability of tasks actively calling for our attention that our ancestors would never have experienced.

The Multitasking Myth

Maybe you look at the picture of pervasive computing I’ve painted and think to yourself, “So what’s the problem? Now we can work anywhere and be more efficient about getting things done.” I agree that we should be wary of the urge to yell “Get off my lawn!” at any unfamiliar, new technology or social trend. And it is undeniable that there are many benefits to our modern digital world. The ability to find information on most matters quickly wherever we are is also a tremendous boon, and our ability to connect with each other without the constraints of time or proximity has countless advantages for collaborative work, commerce, and fun.

Thus, this discussion is not to say that the digital age is bad from the perspective of productivity or even cognitive control. That is surely not the case. It is not the availability of tasks or social connectedness itself that is negative. Rather, it is the pervasive, often intrusive, opportunity for multitasking that can be problematic. And there is one simple reason this is a problem: we are bad at multitasking, and I mean not just a little. We are really bad at multitasking. And the people who think they are good at multitasking are the worst at multitasking.

We often think of multitasking as being more efficient because we are doing two things at the same time. After all, if two different people did two tasks at the same time in parallel, then they would get twice as much done. So, one multitasker should have at least some of the same benefit, right? Wrong.

Try the following. Time yourself saying the alphabet aloud as fast as you can. Then, time yourself counting to 26 aloud as quickly as you can. Add these two times together. This is your pure task time. Now, time yourself alternating between saying the alphabet and counting to 26, as in “A-1-B-2-C-3 …” and so forth. That is your multitask time.

The difference is dramatic. If you even tried the alternating version, you didn’t really even need to time yourself to see the effect. It takes you much, much longer to get through the alternating sequence than the pure ones, and the alternation is experienced as much greater mental effort; that is if you finished at all (statistically speaking, several readers were interrupted by a text message while doing this demo).

Experiments of this form easily falsify the hypothesis that multitasking is efficient. Pick any two tasks. If there is a benefit to multitasking, it should take less time to do the two tasks at the same time, than by themselves, as in all one and then the other in series. Yet, the consistent result from experiments like this is that not only is there no benefit to multitasking, but people take longer and make more mistakes when they do two or more tasks together.

The cost of multitasking is evident in almost every domain where it has been tested. Multitasking among university students whether during class or doing homework is correlated with longer study times and a worse GPA.[6] Studies of workplace productivity show that time is added to tasks, and efficiency is reduced by multitasking.[7] That study of Android users found that interruptions added a time cost when the user returned to the original task that was longer than the average app runtime.[8] The implications of these data are sobering. If we add all these time costs together, we likely spend hours of our week just paying the cognitive overhead of multitasking, rather than making progress toward our goals.

Of course, a limitation to these real-world observations is that we do not know what is cause and what is effect in these situations. For example, is it the case that students who multitask lower their GPA because of their multitasking? Or is it the more distractible students who multitask more and also tend to have lower GPAs? The answer is likely a bit of both. But proper experimental studies that randomly assign people to multitask versus single-task conditions establish that multitasking causes worse performance.

As one example, University of Utah psychologists Jason Watson and David Strayer tested 200 undergraduates’ ability to multitask while they operated a driving simulator.[9] For this study, they used the same kind of immersive and realistic car simulator used for police training. The participants’ task was to follow a pace car on a highway that would intermittently brake, akin to the stop and go flow of heavy traffic.

A secondary task was performed on a cellphone. This task was an adaptation of a standardized test called operation span, or the OSpan task. The OSpan task measures working memory capacity and is a strong predictor of cognitive control function and fluid intelligence.[10] In this version of the test, the participant hears multiple series of two to five words to remember, like “bell,” “dog,” “pin.” The words are interspersed with arithmetic statements that the participant has to verify. For example, they might hear the statement “2 × 2 + 1 = 4?” to which they would reply “no.” At the end, they recall the words they heard (bell, dog, pin) in the order they were presented. They are scored based on the number of words remembered.

Participants performed the driving and cellphone tasks either separately or at the same time. Further, this study used a hands-free headset for the cellphone task, so any issues with multitasking could not be attributed to problems holding a phone while driving.

Performance on both driving and OSpan tasks was worse in the multitasking condition. When multitasking, people were slower to brake when the pace car stopped and so maintained a longer following distance. Do you ever wonder why that person ahead of you in traffic is keeping a 17-car following distance while going 10 miles per hour? They are probably on their cellphone. Compromised driving was evident even when participants were instructed to prioritize the driving task.

Another striking feature of this study was the consistency of multitasking costs. Averages don’t tell us much about prevalence of an effect in a population. But in this study, 97.5% of the participants exhibited worse performance when multitasking. Multitasking costs are evident not just on average; they are prevalent and present across just about everyone.

What was going on with the other 2.5% who did not show a cost in the multitasking condition? Watson and Strayer called this group “supertaskers.” These individuals showed no multitasking cost across all the measures taken. Moreover, supertaskers were superior to the rest of the group when they performed the tasks separately. Using simulations, Watson and Strayer estimated that this perfect time-sharing and outstanding performance across all tasks and measures would happen by random chance only 0.16% of the time. In other words, the 2.5% is a higher percentage than we would expect if this observation was just random error.

The supertasker observation is interesting. It is conceivable that there are people who are more adept at general multitasking than most of us. However, one should be cautious about drawing strong conclusions from this study. Additional work is needed to establish the reality of supertaskers. For example, one would like to see how supertaskers do in multitask settings other than driving and talking on cellphones. Further, subsequent work has not located similar evidence of supertaskers. Nonetheless, even if supertaskers do walk among us, they are exceedingly rare individuals. It is unlikely that you or I am one of them. Rather, it is safe to assume we experience a performance cost anytime we multitask.

Though we all experience a cost, it is not the same for everyone; there are stable differences across individuals in the degree of this cost. One large multisite study tested individual differences in multitasking ability across more than 500 adults.[11] Each participant’s ability to multitask was assessed using three complex tasks designed to test multitasking, each of which required multitasking in a different situation or context, and each was quite different in its specifics.

Despite these differences, the performance of individual volunteers tended to be consistent across the tests of multitasking. In other words, if a person was better than average at one task, they also tended to be better than average at the other two. Thus, based on these results, some general multitasking ability may be common to different multitasking situations. Importantly, however, there were no supertaskers in this study. Everyone was negatively affected by multitasking, but some were simply less affected than others. Second, the correlations among these multitask tests were not perfect. In other words, these different multitask settings also tapped into individual abilities distinct from any general multitasking ability.

Perhaps not surprisingly, this study showed that the general ability to multitask is related to one’s individual capacity for cognitive control. However, no particular facet of control appeared uniquely tied to this ability. Individual differences in multitasking will be both general and specific. Some people will be better overall at multitasking than others, but in any one situation an individual might diverge from this general pattern. For example, a person might be better than most at multitasking while cooking, but in no other situation. As a consequence, it is difficult to predict for any one multitask setting whether a person’s performance will be more or less compromised.

So, rather than writing “Multitasking” under skills on a résumé, the more accurate thing to say would be “Less atrocious at multitasking than others, though there are specific situations in which I will be unpredictably better or worse.” That doesn’t exactly dance off the page, but it is accurate. It’s likely best to leave multitasking off entirely, however. You don’t really have that skill, because no one does.

You are now convinced, I hope, that we are bad at multitasking, that you shouldn’t multitask, and that like everyone else, you will anyway. So, why are we so bad at multitasking? The reason is that multitasking puts us between a cognitive rock and a mental hard place. First, we have constraints on the brain’s ability to do two tasks at once, in parallel. And so, when doing more than one task, we are mostly forced to work serially, switching back and forth from one task to another in time. But when we alternate in this way, we still pay a cost of switching from one task to another, so switching frequently makes us perform worse, as well. To understand more about the source of these costs, let’s delve a bit deeper into the mirror problems of dual tasking and task switching.

Bottlenecks in the Brain

Dutch psychologist and computer scientist Niels Taatgen is one of the world’s foremost experts on multitasking. He notes that there are certainly situations in which we can multitask effectively, as in the proverbial case of walking while chewing gum. But, there are others, as we have discussed, in which we are unable to do so. To gain some intuition to account for this difference, he analogizes the brain’s problem of multitasking to the scenario of a chef working in a kitchen.[12] In a kitchen, some tasks can be done in parallel. For example, one can chop carrots while the oven is preheating. However, other features of the kitchen are bottlenecks. These have a limited capacity in that they can handle only a few tasks at a time. For example, the oven has limited space to heat items at a particular temperature. Indeed, the chef herself or himself is a bottleneck. She or he can either chop those carrots or sauté the onions, but not do both at once. Once the capacity of a bottleneck is full, other tasks needing this bottleneck cannot proceed until the work occupying it is finished.

A long history in cognitive science and neuroscience suggests that the brain faces a similar problem when we try to do two tasks at once. And it is likely that we have more than one bottleneck in our system when trying to complete a task. For example, tasks that require use of our hands, mouth, or feet cannot be performed simultaneously with a task requiring the same body part at the same moment. Try as you might, we can’t say the words “walk” and “bubble gum” at the exact same time and be understood.

Input systems are similarly limited. We can fixate on only one location with our eyes at any moment in time. We can move our eyes around quickly to take input from multiple locations, and our brain fits them all together so that we have the impression of a single, cycloptic vision of the world about us, but this continuity is something of an illusion. The fovea is a small area of the eye’s retina where vision is most acute. Thus, we need to constantly move that fovea around by moving our eyes to take in the world about us, all while the brain knits these inputs together and makes reasonable guesses about the rest.

These physical bottlenecks over our hands and eyes are not the only points of limited capacity, however. The processing that occurs between our ears is also subject to bottlenecks, but these central processing bottlenecks have been trickier to pin down and are seemingly more fluid across situations.

Early ideas about bottlenecks arose from an interest in attention and how we select small portions of the massive inputs to our perceptual system to process. Renowned British psychologist Donald Broadbent became interested in attention from his experience with British air traffic controllers during World War II.[13] These radar operators would track and communicate with multiple pilots at once, but they would do so over a single loudspeaker.

Broadbent wanted to understand how these operators could pick out and respond to streams of information coming from particular pilots while ignoring parallel irrelevant and distracting streams. To better understand this problem, Broadbent conducted experiments in which people would be asked to attend to one stream of input while ignoring another. For example, they might be asked shadow a stream of speech played to one ear while ignoring another stream of speech played to the other ear.

Experiments of this type yielded two major insights. First, people require only a minimal perceptual difference, like a tone of voice or loudness difference or location to distinguish a desired stream of information from irrelevant streams. Second, very little at all was apparently registered about the irrelevant streams of information. People might be able to report back the basic perceptual characteristics of the irrelevant speech, like its loudness, but they had not retained the content of what was said.

For Broadbent, this suggested we have limited capacity to process inputs in parallel. In other words, the bottleneck comes very early in perceptual processing. He proposed that simple, concrete features, like the tones and intensity of a sensory input, can be processed in parallel, without a bottleneck. But any further perceptual or cognitive processing, such as of words and meaning, is capacity limited and must be processed serially. This early bottleneck defined this class of so-called early-selection theory.

Subsequent research, such as by the psychologist Anne Treisman and others, challenged a strict early bottleneck by demonstrating that some information did, in fact, make it through to higher processing.[14] For example, people could notice an occurrence of their own name in an ignored speech stream. Recognizing your name is not possible unless you are, in some way, processing the words coming through the other streams beyond just the sound. These observations led to a range of new ideas that ran the gamut from adaptive early bottlenecks that could regulate the amount of parallel versus serial processing in the system, to late-selection models that placed the bottleneck at the point of response selection with massive parallel processing up to that point.[15]

The neuroscience of attention shed some considerable light on this controversy. When people pay attention to a particular stream of information, neural activity is modified such that neurons firing to the relevant information are enhanced in a number of ways, while neurons coding distracting information are suppressed.[16] To test where along a processing stream attention has its effects, then, neuroscientists have measured when these changes in neural activity occur in people or animals while they attend to some streams of information and ignore others.

Taking this approach, it is evident that neural activity can be modulated by attention at multiple points along the input stream, both early in processing, at least as early as the first landfall of sensory input to the neocortex, as well as late in processing, when percepts are translated into responses, and motor commands are issued. At what point or points this modulation happens depends on a combination of multiple factors relating to the nature of current tasks and goals and the inputs themselves. Thus, there is no single point along the input stream where a bottleneck limits parallel processing. Bottlenecks can occur at multiple places.

What constrains where a bottleneck will occur, then? In a seminal review of these neuroscience data, attention scientists Bob Desimone and John Duncan provided an answer to this question.[17] They proposed an account of how attention and bottlenecks might work in the brain through a process they termed “biased competition.” Desimone and Duncan argued that the brain is capable of processing a large amount of information in parallel. For example, neurons in different areas of occipital and temporal cortex will process information about perceptual features, like form, color, motion, and so forth, mostly in parallel. Likewise, information about the shape and form of objects can be processed along a stream from occipital to ventral temporal cortex, while information about how to act on those objects in space can be processed in parallel more dorsally in occipital and parietal cortex.

However, amid all this distributed processing, there will also be points where the processing streams overlap, and the same population of neurons is required for processing two distinct input streams. Think of a vast and sprawling road system. The larger it gets, the greater the opportunity for cars to drive without ever meeting, processing their drives in parallel. However, there will also be lots of intersection points where cars might meet. At these points, there is competition, and traffic lights or stop signs are needed.

In the brain, these points of competition can be resolved “top down” by goals, such as your desire to process auditory information from your hands-free cellphone rather than the visual input of the road in front of you. Or they can be resolved “bottom up,” such as by learned tendencies in the input stream itself, like hearing one’s own name. Thus, these signals bias the competition among the inputs such that one set wins over the other. The winner is available to influence further processing, while the loser is suppressed.

This biased competition dynamic relies on essentially the same principle as the CDM model introduced in chapter 3. In that case, an overlap at the response stage caused competition that could be resolved either bottom up, based on the learned tendency to read words, or top down, based on the task context maintained in working memory. The biased competition model further acknowledges that there are multiple potential bottlenecks or points of conflict as information propagates in the brain.

The implications of such a scheme for multitasking are clear. If two tasks require processing more than one stream of input at a time, we will be forced to shift our attention from one stream to the other to resolve any conflict points. In doing so, we may fail to register important changes in the world, like when that car in front of us stops, or a pedestrian steps in front of our car. For dynamic kinds of multitasking, then, basic limits on our ability to attend multiple competing inputs will play a role in our poor performance.

Importantly, the problem of conflict is not just with adjudicating between different streams of perceptual input. The rules, sets, and responses associated with tasks also compete with one another in multitask settings. As evidence for this type of central processing bottleneck, another important line of studies has focused on a phenomenon termed the psychological refractory period, or PRP effect.

In the basic PRP procedure, people are given two simple tasks to do at once, such as categorizing a light as bright or dim and a tone as high or low. People indicate their categorization for one task on a keypad and the other task using a vocal response. In this way, there is no overlap of the physical input or output organs people are using for the tasks, and so any bottleneck is due to whatever processing happens between these input and output devices.

The tone can appear before the light, with a short interval of time between them called the stimulus-onset asynchrony, or SOA. As the SOA gets longer, there is more time for the tone task to make its way toward a response before the light task begins. In this procedure, the critical test for a bottleneck comes from the impact of the SOA on performance of the second task, the light categorization in our example.

Experiments of this form consistently find that when the SOA is short, the second task takes longer than when it is performed alone or when the SOA is long. This lengthened response time is termed the PRP effect and is evidence of a bottleneck in processing the two tasks at once.

To understand the logic behind this conclusion, think of the two tasks as trains starting at different locations representing the stimuli, called light and sound, and traveling to different destinations representing the responses, called hands and mouth. If the routes they travel are of equal length and entirely parallel, and the trains always move at the same speed, then any difference in arrival time between them will be equal to the difference in their start times. In other words, if the start of the second train is delayed by 30 seconds relative to the first train, it will also arrive 30 seconds after the first train.

Now, consider the case where there are points at which the tracks are shared by the two trains. At these intersections, there is a bottleneck, because only one train can go through at a time. If they both arrive at the intersection at the same time, one train has to wait on a siding while the other goes through.

For example, let’s say that the first train is sufficiently long that it takes 40 seconds for it to go through the intersection. If we delay the start time of the second train by 30 seconds, the first train will still be in the intersection when the second one arrives there. So, the second train will have to wait on the siding for 10 more seconds while the first train finishes going through. As a result, even though the second train started only 30 seconds after the first, its arrival time relative to the first will be delayed by 40 seconds because of the bottleneck. By contrast, if we delay the start of the second train by 45 seconds or one minute or any time greater than 40 seconds, the first train will have already made it through the intersection by the time the second train gets there. In this case, the second train will not experience any additional delay in its arrival time beyond its start.

Following this logic, we can measure the impact of a bottleneck by measuring how SOA affects the time to complete the second task. If processing the two tasks is entirely parallel, then the time to complete the second task will be the same across SOAs. However, if there is a bottleneck, we will see a cost of starting the second task too early, like adding 10 seconds in our train example. This cost will be highest at the shortest SOA and will reach an asymptote at the point when the SOA is longer than the processing time of the bottleneck.

Dual-task experiments using this logic have consistently found a cost to complete the second task at short SOAs. This PRP effect gets smaller at longer SOAs in proportion to the size of the SOA. Thus, this procedure provides strong evidence for a bottleneck somewhere along the pathway from the stimulus input to the response output that impedes our ability to perform two tasks at once.

Numerous experiments using the PRP procedure have tried to identify where along the processing pathway this bottleneck exists. Perhaps mirroring the debates over late versus early selection, there has been evidence for bottlenecks at multiple stages of processing, from perception, to movement, to the mapping of stimulus to response. Further complicating matters, there is evidence that the second task can also affect the response time of the first task.[18]

The most straightforward interpretation of these data is that there are many potential bottlenecks depending on the tasks you are trying to time-share. Trying to find the one and only stage at which a bottleneck occurs is probably misguided. Rather, following the insight of the biased competition model, there are many potential points of overlap within the brain when shared neural representations are being used. Any of these overlap points can yield bottlenecks under the right conditions.

Although points of overlap will occur at multiple points in processing, it is also the case that some kinds of neural representation may be more vulnerable to being a source of overlap than others. And the task demand representations used by control systems appear to be one such common source of overlap across many tasks. This stands to reason. As we discussed in chapter 2, control systems are adapted to be general and operate across multiple situations to solve novel tasks on the fly. To be usable in many situations, the high-level contexts, rules, and compositional action policies used by our control system are abstract. However, this generality also means that more than one task may need to use the same abstract representation, which produces overlap in control representations and becomes a bottleneck when a person is doing more than one task at once. Thus, though the frontoparietal control systems are not the only source of processing bottlenecks, they are a highly common one.

Cognitive neuroscience research has provided evidence that the frontal cortex may indeed be a major locus of conflict when two tasks are being done at once. René Marois and his colleagues at Vanderbilt University have conducted one such rigorous line of studies that have identified the frontal cognitive control network as a bottleneck in the brain. In multiple experiments, they scanned their research volunteers with fMRI while they performed various dual-task procedures.

Marois and his colleagues reasoned that if a part of the brain was the locus of a bottleneck, then it would satisfy three criteria during dual-task performance.[19] First, it would be active in all tasks, despite superficial differences in, for example, verbal versus manual responses or visual or auditory stimuli. Second, its activity would be sustained for longer during dual-task conditions relative to single-task conditions, reflecting the processing time spent occupied by both tasks. Finally, however, the onset of the activity would be the same regardless of dual tasking (i.e., the first train reaches the bottleneck in the tracks at the same time regardless of the presence of a second train).

Across their different experiments, Marois and colleagues found that the frontoparietal network consistently satisfied their three criteria for a bottleneck.[20] Further, when Marois and colleagues manipulated the difficulty of the control demands across the tasks, they observed not only larger dual-task costs but also a corresponding change in the duration of activation in the frontoparietal network. This directly implicated the frontoparietal network as a bottleneck for control in this dual-task setting.

Importantly, however, our brain’s bottlenecks are not immutable. It was initially thought that the control bottleneck was unavoidable and unchanging. However, there is now ample evidence that with training we can reduce costs for specific dual-task situations.[21]

To clarify, there is no evidence that this training results in a general multitasking ability. General multitasking ability would transfer to all multitasking situations regardless of the specific tasks involved. As we will discuss in chapter 9, there is little evidence that one can improve all-purpose multitasking. Rather, training improves multitasking costs for highly specific dual-task situations. In other words, if we practice two specific tasks enough and do so under similar enough conditions, there is evidence that we start to reduce the costs involved in time-sharing those particular tasks.

Kelly Garner and Paul Dux at the University of Queensland investigated how the brain changes in these cases of training.[22] They trained one group of volunteers to perform two simple single tasks, and a dual task that combined both. To train, people repeatedly performed the tasks by themselves and the tasks together. In fact, they practiced the tasks for more than 1000 trials each over three training sessions. A control group performed thousands of practice trials of three tasks unrelated to the dual task. Both groups of participants were then scanned during an fMRI session while they performed the single and dual tasks. Scanning took place once before and once after the training.

Replicating prior work, the training was effective for those who practiced dual tasking. Though they still exhibited a cost of dual tasking, their cost was reduced relative to that of controls. Further, activity throughout the frontoparietal network was reduced for the training group relative to that of controls. In other words, these reductions in dual-task cost were accompanied by reduced activity in the same network of areas identified in the earlier work in association with the bottleneck.

Garner and Dux next analyzed the specific patterns of activity in the frontal cortex while the volunteers performed the single and dual tasks before and after training. Notably, the researchers found that the patterns of activity in the frontal lobe related to each task were more distinct from each other after training; there was less overlap. Garner and Dux proposed that the dual task training made the frontal neural representations used for the single tasks more distinct. No longer was the control system relying on common, abstract context representations to perform both tasks. Rather, after training, it was relying on unique representations specific to those particular tasks. Thus, the control system may not be less involved in the task per se; rather, it is less susceptible to interference, because there is less overlap in the neural pathways involved.

Abstract representations are the backbone and currency of a generative and compositional cognitive control system. By assembling compositional representations on the fly, we are able to solve new problems and achieve the hypothetical goals we generate. But these abstract representations come with a cost. As they are components of multiple tasks, they are also a major source of interference when we do multiple tasks simultaneously. As we perform a task repeatedly and automate that task, we may, through mechanisms of cortical plasticity, come to represent that task using more specific neural populations. A shift away from abstract, generalizable neural representations that are susceptible to competition and interference, and toward more task-specific representations that can be used in parallel, might hint at the kinds of changes that underlie the shift toward automaticity. These tasks might also suffer less from the negative effects of dual tasking, though only when they have been trained together.

Of course, in most multitask settings, we lack the opportunity to train the specific dual-task situations enough to get us out of our bottleneck, and we certainly can’t do it on the fly for novel pairings. Rather, we are forced to hold abstract contexts in working memory. To avoid interference, we must operate serially at some point, using our gating system to select certain representations while others remain pending in the ways we discussed in chapters 3 and 4. The problem is that task alternation also takes a toll on performance. This tax on serial task performance is called the task-switch cost.

Mental Set and Shift

In 1927, Arthur T. Jersild received his PhD in psychology from Columbia University. He would go on to become an influential child psychologist, writing a major textbook and conducting a landmark study of 3000 children establishing evidence that even very young children are self-aware, which evidently surprised everyone in 1952.[23] However, his dissertation work, entitled “Mental Set and Shift,” concerned the costs of switching from one task to another in the context of multitasking.[24]

Jersild’s approach to this problem was straightforward. He gave his research subjects lists of two-digit numbers. For some of the lists, his subjects would perform a simple operation such as adding 6 to each number on the list as quickly as possible. On another list, they would subtract 3 from all the numbers. Importantly, a third set of lists required alternation, adding 6 to one number, subtracting 3 from the next, adding 6 to the next again, and so forth down the list. He measured the time it took his participants to complete each list, with some penalty for errors, and compared the completion times between the single-operation lists and the alternating lists.

The overwhelming result from this experiment was that completion times were substantially longer for the alternating lists than for the single lists. You experienced this alternation cost yourself when you alternated saying the alphabet and counting to 26. Likewise in Jersild’s experiment, alternation between tasks induced a cost in time to completion.

Jersild was not particularly surprised by this result. In fact, Jersild’s experiment was not the first documented evidence for a task-switch cost. The consensus among psychologists at the time was that shifting tasks induced an unavoidable cost in performance. Jersild cited precedents for this effect in the applied psychology surrounding the industry trends of his time, perhaps best exemplified by the great assembly lines at the Ford Motor Company.

Psychologists wanted to understand the gains in efficiency Ford achieved when he abandoned the concept of a single engineer doing all the tasks required to build a car and employed a team of specialist line workers, each repetitively performing a single task on a moving line. For example, Ford specified 84 steps for building a Model T.[25] He trained each line worker on only one of these steps until it was automatic. The worker would perform that task as rapidly possible, timed to its maximal speed by the moving conveyor. The result of each line worker performing their task at top speed and not switching to others was a massive increase in time to car completion.

In light of the efficiency of this system, psychologists assumed that the switch cost was obligatory. The only way around the task-switch cost was to not switch tasks. Thus, Jersild fully expected an alternation cost when a person switched tasks repeatedly. His novel insight was to find conditions under which this alternation cost was reduced or eliminated. In this, he was well ahead of his time.

Jersild proposed that switch costs had little to do with the requirements of shifting itself; rather, they stemmed from the nature of what he termed the mental set. His premise was that each task involves various mappings from input conditions to responses. Just as we’ve drawn analogies to driving or response pathways, Jersild drew an analogy to a switchboard with connections being properly set up for each connection between individuals. A mental set comprised the collective consideration of these connections required for a given task. Jersild proposed that to shift tasks required reconfiguring these connections over the switchboard. So, to the degree these mental sets overlapped or interfered, more connections would need to be reconfigured, and so there would be a cost of switching. Conversely, if mental sets associated with two tasks were sufficiently separate and distinct, then the cost of shifting between them would be reduced.

To test his hypothesis, Jersild used a similar list-completion procedure as before, but this time he made the two tasks distinct. For example, he gave his subjects lists on which they had to shift between subtracting 3 from a number, naming the color of an ink patch, or saying the opposite of a word. Unlike in the first experiment, these tasks are very different from one another—you can’t subtract 3 from a color patch. And you can’t find the opposite of the number 42. As a consequence, these tasks have entirely distinct mappings between inputs and outputs. Thus, the opportunities for a bottleneck to occur are reduced.

With these new lists, Jersild found that the shift cost was eliminated. At a time well before sophisticated psychometric methods and computational models of the brain were available, Jersild demonstrated that switch costs are not mandatory, and they relate in some way to the overlap of the task sets involved.

In the years that followed, Jersild’s basic experiments were replicated, improved, and extended using more modern methods and better-controlled designs.[26] An influential view to emerge from the line of studies that followed Jersild’s was that switch costs reflect the time it takes for the system to internally reconfigure one mental task set to another.

Importantly, a reconfiguration process also necessarily assumes there is a “task state” to reconfigure. In other words, this perspective requires our brain to be a finite-state machine, that is, a system that will reside in one state and stay there until something comes along to push it to another state.[27] A light switch is a classic example of a simple finite-state machine. It stays on until someone pushes it off. Thus, in this framework, the brain remains configured for one task until an input or control process comes along and reconfigures it to do another task.

In the strongest version of this finite-state framework, then, the switch cost is directly attributable to the time needed by a cognitive control process to shunt the system from one task state to the other. This reconfiguration process presumably remaps the links between inputs and outputs to accommodate the new task given the state of the previous task. The time-consuming machinations of this internal task set reconfiguration process are an explanation of the costs of task switching.

Some of the strongest evidence for this perspective came from a seminal line of experiments by Robert Rogers and Stephen Monsell.[28] In their experiments, they showed their participants simple letter-number pairs, like “a3,” that appeared in a predictable pattern around a grid on a computer screen. When a pair appeared in the top squares of the grid, people performed one simple task on the pair, such as categorizing the letter as vowel or consonant. And when the pair appeared on the bottom, they performed another task, such as categorizing the number as odd or even. Thus, as a natural consequence of where a pair appeared on the grid relative to its last position, people either repeated or switched the categorization task. Moreover, because the movement was predictable, switching and repeating were also predictable.

Rogers and Monsell reasoned that if there is an active, internal switching process, it could take advantage of the time before the next pair appeared in a predictable location to reconfigure the task set in advance. This prediction was borne out by the data. If people knew they had time to prepare for an upcoming switch (and they took the opportunity to do so), then their switch cost was reduced in proportion to their preparation time, though there was always a little residual cost. Nonetheless, the reduced cost appeared consistent with a voluntary and time-consuming reconfiguration process that contributed to the switch cost and could be reduced with preparation.

In parallel, however, a line of work by Alan Allport was demonstrating that the switch cost is affected by factors beyond reconfiguration, namely, interference between the tasks themselves.[29] Allport and his collaborators demonstrated that any overlap in the environment between a task being performed and a competing task performed recently induces a switch cost. Merely reintroducing a component from that earlier task, like a visual feature or stimulus, is sufficient to produce the performance cost.[30] This occurs even when a long time has passed since the competing task was last performed. Presumably, by that point any task-set reconfiguration process would have long since finished rewiring the brain state. The only way to explain the cost, in this case, is that cueing the previous task interferes with performance of the present task.

The insight from Allport’s experiments was crucial. When we perform any task, we form an association between that task and the materials arising during that task, be they the stuff around us, the stuff we are thinking about, or the stuff we do. When I make coffee, I come to associate all the sights and sounds in that task with the task of making coffee. If any of these materials are encountered again, then I am likely to retrieve the coffee task into memory, at least to some small degree. This echo from other tasks causes interference if the retrieved task shares a bottleneck with a current task to be performed. The switch cost, then, is attributable not only to time spent reconfiguring the system but to interference from another task. Control can resolve this interference.

The PFC and its associated network for cognitive control are an obvious candidate to reconfigure task-sets, and on first pass, the evidence does implicate the engagement of frontal control systems during task switching. In fMRI studies of task switching, when one compares a moment when the task switches to one when it repeats, the consistent observation is increased activity in the frontoparietal control network.[31] Patients with damage to lateral PFC tend to show larger switch costs than do controls when task switches occur between two task sets that overlap, and when the cues for the correct task to perform are ambiguous.[32] People with Parkinson’s disease, likewise, are notoriously inflexible and have difficulties shifting to new responses or tasks.[33] Parkinson's affects both frontal cortical and striatal function. Further investigation, however, has suggested that though the PFC plays an important role in helping us switch tasks, it may be particularly crucial in reducing the task-set interference.

First, there is evidence that recent tasks can interfere with a current task by activating competing neural representations. Nick Yeung and Jon Cohen scanned a group of people with fMRI while they switched between two simple categorization tasks: a “face” task in which they would decide if a face was male or female, and a “word” task in which they decided if a word has two syllables or not.[34] Throughout the experiment, people always saw a word superimposed on a face, so the stimulus itself did not tell them which task to do. Rather, a colored box would periodically appear that cued which task to do for the next run of stimuli until the next colored box appeared. As a consequence, after each colored box cue, the task switched, and people accordingly paid a switch cost on the next item.

Yeung and Cohen had a specific reason for choosing faces and words as the stimuli in this experiment. It is well established that when people are viewing faces, there is activity in a specific area of posterior neocortex called the fusiform gyrus. Further, the more that people process, attend, or think about a face, the more active this area will be, so activity in this region is a proxy for “thinking about faces.” In this experiment, a face was present on every trial. Nonetheless, activity in this area of the fusiform was more active when people made the gender categorization, because they were paying more attention to the face during this task.

Importantly, when the task switched from the face to the word categorization, the fusiform face area was also more active right after the switch. There is no reason to attend to the face during the word task; if anything, pains should be taken to ignore it. Nonetheless, recently performing the face task resulted in a carryover to the word task. And the more active the face area was after the switch, the larger the switch cost in performance. Thus, the switch cost was attributable to the irrelevant, competing task being active in the system.

The interference occurring during task switching may also weaken our ability to hold the relevant task context in our working memory. Cognitive neuroscientist Tobias Egner and his colleagues at Duke University found that following a task switch, the neural pattern for the current task was weakened owing to noise from the previous task representation. So, though there is greater activity overall in frontoparietal control areas, the specificity of the activity pattern corresponding to the relevant task itself is noisier. Weakened in this way, the memory of the current task context cannot provide as much support for the task following a switch, making the system more vulnerable to interference.[35]

In summary, when we switch tasks, our ability to perform our new task may be compromised by interference from the task we performed recently. Indeed, the mere association of that other task with materials in our environment will cause this other task to come to mind and cause mischief. When we alternate tasks repeatedly, we are typically not changing our environment along with our change in task, so we are forming associations between items in our environment and more than one task. Each of these items then acts as a cue to multiple tasks that can run around in our head, causing noise in the background and interfering with whatever we are doing.

Think about how many tasks we do at the office. All around us are cues to multiple tasks other than the one we are doing right now. Maybe this is partly why we often have an easier time doing certain tasks away from our office. A change of scenery may serve more of a purpose than we give it credit.

As noted in chapter 4, our brain is a dynamical system. It is rarely in the exact same state when we perform a task. The control problem is always how to sustain a task goal despite these dynamics. That problem is made harder when we switch tasks because of the echoes of what we were just doing or what we have done before.

We are bad at multitasking. However, understanding cognitive control gives us a deeper understanding of the causes and limits of this simple maxim. In essence, dual-task and task-switch costs arise from the same source: multiple different tasks share some portion of their response pathway. These points of overlap cause competition and interfere with performance. In the case of dual tasking, one task will have to wait until the other is done before it can proceed. However, task alternation does not fully help, because a recently performed task is likely to reactivate, causing interference with the task we are trying to do anyway. These echoes of other tasks are held at bay by task representations maintained in working memory that provide support to the current task. But the task representations compete in working memory, too, putting pressure on output gating mechanisms to favor the right task representation.

Why is our system so clunky and vulnerable to all this interference? Shouldn’t we have evolved to have massive multicore CPUs capable of multithreading our ham-sandwich-making alongside our cat-picture-posting? One reason this may not be the case is that evolutionary pressures favored generality in our ancestors. Our general ability to get things done comes from a system capable of building any task we can think of on the fly out of a library of abstract, compositional representations that come to mind readily when cued. However, the very generality of these representations makes it likely that any two complex tasks will rely on at least some overlapping representations. As a consequence, they will interfere with each other and will require control systems to resolve this interference. To return to our analogy of a road system, the routes taken from any two start points to any two destinations are likely to intersect at some point, particularly at major hubs. This overlap will be a source of interference whether we are trying to perform two tasks at once, like talking on our cellphone while driving, or when we try to switch tasks, because of the echo of the previous task in our system.

Making matters worse, the tasks we perform are often lumped together arbitrarily. This makes multitasking unlike the case of hierarchical control discussed in chapter 4, in which superordinate contextual representations are built into the task representation to keep task components from overlapping. It is also unlike the case of multitask training in which we practice something hundreds or thousands of times until it gets its own specialized representation that can run on an express track entirely separate from the other task. On the contrary, in our modern world, multitasking mostly imposes itself on us, on its own terms. We don’t plan to get that push notification on our phone in the middle of making dinner, and little changes on our desk as we shift from writing a report to answering emails. It is all there, a world of cues constantly waving at us and pointing excitedly to other irrelevant tasks. Thus, the control system is adapted to performing the task we want to perform, staying on set, and behaving efficiently in the face of all this background noise.

That no particular control process is directly responsible for multitasking also puts the discussion of “multitasking ability” into perspective. It is unlikely that anyone will be able to train a general multitasking ability, because there is nothing there to train. However, as control is important for resisting the impact of interference among multiple tasks, the better you are at cognitive control, the better you will be at multitasking, generally. This is not because you have a fast reconfiguration process or a magnum-sized bottleneck but because you are going to do a better job overcoming the inevitable interference that arises in multitask settings.

Finally, we return to a point that Jersild had correct back in 1927. Reducing interference from other tasks by reducing overlap is the best way to reduce the costs of multitasking. This was the simple genius of Eugene O’Neill’s approach to multitasking. Sure, he was switching between writing two major works of art, but he kept the context of these two tasks as independent as possible. The unique little pictures and baubles on each desk may have helped rather than hindered his multitasking. Each one would have been a cue, a unique reminder of one play, one task only. As he sat at one of those desks, everything about his environment pulled his mind back to the correct task and helped him stay there. It was an enviable situation, really. If only we all had just two tasks to do and enough desks on which to do them.

CHAPTER 6

Stopping

Down by 1 point and with seconds remaining of the 1982 NCAA basketball national championship game, Georgetown’s Fred Brown had the ball and a chance to win the game. Then, in one of the most memorable gaffes in college basketball history, he passed the ball directly to the other team, to the Tar Heels’ James Worthy. Brown had this to say after the loss, “It was only a split second. But, you know, that’s all it takes to lose a game. I knew it was bad as soon as I let it go. I wanted to reach out and grab it back. If I’d had a rubber band, I would have yanked it back in.”

Where there is human behavior, there is human error, and so stopping ourselves from doing something wrong is a necessary part of surviving as a behaving organism. Indeed, inaction is often precisely the action called for in a particular situation. A national championship isn’t always on the line, but we’ve surely been in situations where we needed Fred Brown’s rubber band. A truck might rumble around the corner just as we are stepping into the crosswalk. We might want to hold back tears or a burp. We might try not to think about that argument with our friend or a memory that has haunted us. We may need to wait to speak, to hold our temper, or to avoid staring at that mole on someone’s face.

Inhibition is the control process commonly given credit for these various cases of stopping. Whether in overt behavior or thought, inhibition is conceptualized as a suppressive or restraining force that stops another propulsive force, be it a motor response, an impulse, a thought, or an emotion. And if inhibition is removed, the thoughts and actions once restrained are free to reemerge.

In this common conception, inhibition has been closely associated with self-control, temperance, and orderly conduct even before people had terms like “cognitive control.” Plato conceptualized the human condition as a conflict between two great stallions, one representing reason and the other desire, and the restraint of desire was essential to the well-balanced soul.[1] Christianity’s traditional doctrine of dualism between the spirit and the flesh strikes a similar theme. Buddhism sees desire as suffering, and freedom from desire as the path to enlightenment. Indeed, ascetic traditions across many belief systems view inhibition of our basic wants and needs as a path to spiritual fulfillment. Taking a vow of silence, engaging in celibacy, or abstaining from food or drink during Yom Kippur, Lent, Ramadan, or Paryushan are all examples of ritualized inhibition for spiritual health rather than temporal purpose. Across many human cultures and systems of belief, control through inhibition exhibits mastery over one’s base animal nature and is the path to a higher state of being.

Human societies, likewise, generally hold in esteem those who exhibit inhibitory expressions of self-control, even outside of religious settings. Self-denial is viewed as a necessary precondition for success in many domains. Listing the “6 daily habits of the world’s most successful CEOs,” Business Insider declared:[2]

Rising early is a nearly universal trait among successful CEOs. New Jersey Nets CEO Brett Yormark gets up at 3:30 a.m. Virgin America CEO David Cush starts the day at 4:15 a.m. Robert Iger, CEO of Disney, says he gets up at 4:30 every morning, and Dan Akerson, the former CEO of General Motors, says he rarely sleeps past 4:30 or 5 in the morning. […] If you want to rise through the ranks and join these big players in the business world, you won’t do it by staying in bed.

It is not just that they are savvy, innovative, lucky, or wealthy to begin with; to the Business Insider, CEOs are also successful because they control their impulses in unusually effective ways. Of course, early rising is also a trait of the person working two jobs to make ends meet, one of which likely starts right around the time David Cush is waking up, but that’s beside the point. Whether for an individual or a society, people generally view austerity as being virtuous, beneficial, and healthy, and they are accordingly suspicious of gains without pains.

So, inhibition is at the heart of colloquial ideas about cognitive control. It wards against impulsive tendencies or oafish behavior. It holds the line against that second helping of dessert. However, though no one denies that we can voluntarily stop our behavior or thoughts, it is not always the case that stopping is a consequence of an actual inhibitory process. As we will see, not every instance of inhibition is attributable to a restraining force. Nevertheless, there is growing evidence that the brain does engage inhibitory functions, and these are critical components of cognitive control. In this chapter, we will explore inhibition more fully. We will consider how we use it to control both action and thought, and how it is supported in the brain.

What Is Inhibitory Control?

Given the importance of inhibition in human culture, it should come as no surprise that the concept of inhibition has also been highly influential in psychology and neuroscience. Inhibition was a centerpiece of psychological theory almost immediately, featuring in such foundational works of modern scientific psychology as the writings of William James.

James saw inhibition as closely tied to processes of the brain, and so the term makes several appearances as a physiological process in his Principles of Psychology.[3] In his chapter on instincts, however, the psychological concept of inhibition comes to play a central role. James proposed that acquired habits inhibit our instinctual tendencies. For James, base instincts, like hunger or pleasure seeking, drive us to pursue certain actions. But doing so leads to habit formation, and once a habit forms, it supplants the instinct, thereby inhibiting it. From that point forward, James suggests, we no longer seek new habits of the supplanted type.

For example, infants instinctively follow their hunger to eat the foods their parents give them. However, having formed a habit of eating chicken nuggets, the older child no longer feels impulses of hunger toward unfamiliar foods. Indeed, the outcome can often be the opposite. A steaming platter of mealworms offered to most Westerners would be met with violent disgust. For James, this shift demonstrates an inhibition of the base drive to eat by our particular habit of what we eat.

James extended this idea of inhibition through habit learning to explain the restraint of most base instincts. Though the concept of critical periods in the development of preferences persists today, no one takes this particular theory of inhibition of instincts very seriously anymore. And indeed, when nudged a bit, James’s ideas start to strain credulity. For example, James proposed that having one’s own home or spouse naturally supplants one’s instinctual desire for other people’s houses or partners. His apparent earnest sincerity in this assertion makes one wonder how much James got out in late nineteenth-century Boston society.

Notwithstanding James’s uncomplicated perspective on relationships, the mechanism he offers for inhibition makes a subtle but crucial distinction. He is not proposing that our instincts are governed by an overt inhibitory mechanism that exerts a suppressive force over our impulses. We are not inhibiting our desire for our neighbor’s home directly. Rather, in James’s psychology, an impulse is inhibited when it is supplanted by some other tendency. Thus, inhibition as an outcome occurs through a mechanism that is not itself directly inhibitory.

In contrast, other psychological schools of the time did propose direct psychological mechanisms of inhibition. Somewhere near the top of this list is Sigmund Freud’s psychodynamic framework. In general, Freud’s program to understand mental illness in secular terms, while of dubious merit as a scientific theory, was notable for its comfortable nearness to Western religious concepts. Inhibition was no exception to this theme and would have been warmly familiar to anyone raised on notions of a tripartite soul or Pauline dramas between the spirit and desire. Freud, likewise, saw the psyche as a conflict between our Id, our Ego, and our Superego. Inhibition adjudicated this conflict by restraining our impulses, lashing that Id down in the basement of our mind where it could not express itself consciously in behavior. When these inhibitory processes go awry and homeostatic balance is lost, the result is psychopathology.[4]

Thus, inhibition for Freud was a direct suppressive force, restraining our base desires and keeping them at bay in accord with our particular norms and dispositions. The desires are themselves ever straining at their tethers, eager to get free. So, a release from inhibition results in pathological expression of the very desires that were most restrained. Inhibition played a similar role in Freud’s theory of defense, in which it was called upon to repress traumas and unwanted memories into the subconscious, where they could work mischief below our awareness. Further, Freud supposed the process of inhibition to be itself intrinsically effortful, requiring exertion to hold down our deeper impulses. It is notable that this last idea has resurfaced in some current theories of cognitive effort or “willpower” that ascribe an intrinsic cost to control. We will address these ideas in more detail in the next chapter.

It is important to emphasize that there is little scientific evidence for these aspects of Freud’s psychodynamic framework. In fact, scientific psychology and neuroscience never really took psychodynamic theories seriously. Their popularity mostly survived in clinical psychology and in the humanities, where Freudian concepts enjoyed a vogue among literary critics. However, any discussion of inhibition would be incomplete without acknowledging Freud’s ideas, because their influence on the thinking of later psychologists is undeniable. Setting aside some of his more fanciful notions, Freud committed to two important dimensions of inhibition that are echoed in later scientific theories.

First, Freud assumed that inhibition is a separate psychological mechanism that suppresses ongoing impulses without removing them. This active suppressive mechanism of inhibition contrasts with James’s concept of inhibition as a consequence of substitution by learning and habit. William James was describing the outcome as inhibitory. For Freud, inhibition was the process itself.

Second, Freud took for granted that inhibition could act on thoughts and memories and not just motor responses. Neurophysiologists had characterized inhibition as a suppressive process but with the target of restraint being a neural response or the antagonism of muscle movements. Likewise, Pavlov had suggested that a conditioned response could be suppressed through other associations, only to reemerge when the inhibition was removed. However, these ideas had focused entirely on overt responses and their link to a conditioned stimulus. In contrast, Freud assumed that inhibition could also apply to mental life beyond motor function, and this point remains controversial to this day.

Inhibition largely disappeared from psychology during the long drought of the behaviorist era in the early twentieth century. Yet, while behavioral psychologists twiddled their thumbs for half a century, neuroscientists carried the torch and continued to debate and research the reality of an inhibitory process in the nervous system.[5] As a consequence of this work, they definitively characterized inhibition as a signaling process between neurons, and they documented its expression at a systems level, including early expressions of the disinhibitory networks of the basal ganglia by the 1950s and ’60s.[6] Thus, neuroscience had located inhibition as a mechanism and defined it in concrete terms.

As behaviorism waned in the 1950s, cognitive psychologists picked up the baton once again and started to revive the concept of inhibition in the mind. Miller, Galanter, and Pribram highlighted inhibition as an explicit mechanism for control flow in the final “Exit” step in their TOTE model.[7] Other cognitive psychologists were directly inspired by the observations of antagonistic systems of excitatory versus inhibitory neural interactions in neuroscience. Suppressive inhibitory influences that countered facilitative influences emerged as a theoretical device in research on attention, memory, perception, and language. In fact, inhibition as a concept turned up everywhere. Anytime a person’s behavior slowed down or they failed to respond in some way, it was ripe for an explanation in inhibitory terms.[8] Often, these inhibitory mechanisms were conceptualized as a control process, the product of a goal-relevant antiforce that stopped an irrelevant or counterproductive process from exerting its influence on thought or behavior.

Inhibition as a cognitive control process was likewise emphasized by neuropsychologists and neurologists trying to understand the deficits that followed frontal lobe injury. These scientists saw a common inhibitory theme in the deficits of frontal lobe syndrome. And some went so far as to propose that inhibitory control might be that elusive unifying function for the prefrontal cortex. The stronger forms of this unitary process idea would later be abandoned, but it is easy to see why the characteristics of even subtle symptoms of frontal lobe syndrome led neuroscientists to associate prefrontal cortex with inhibitory function.

Recall, for example, Brenda Milner’s “curious dissociation” from chapter 1, wherein patients performing the Wisconsin Card Sorting Test are able to acquire the correct sorting rule initially but then are unable to abandon this rule when it changes.[9] They perseverate, apparently unable to stop sorting according to the obsolete rule, even when they are consciously aware that they are sorting the cards incorrectly. This could also be interpreted as a loss of inhibitory control by failing to tamp down the previously rewarded policy.

In their interactions with other people, frontal patients are also notoriously unrestrained and socially disinhibited, talking impulsively and out of turn or addressing socially inappropriate topics or acting out in hypersexual ways.

For example, in his detailed 1935 profile of “patient A,” neurologist Richard Brickner devoted an entire chapter to “impairment of restraint.”[10] Patient A had been a stockbroker on the New York Cotton Exchange before surgery for a tumor in 1918 excised a portion of his frontal lobe. A had the “characteristic impulse to utter whatever thought comes to his mind.” For example, upon meeting someone, he might say, “I don’t see why I should shake hands with you, but I will.” Patient A would talk “without cessation, in hypomanic fashion, showing evident flight of ideas.” Brickner also provided many examples of deeply socially inappropriate hypersexual behavior on A’s part.[11]

All these problematic behaviors might be characterized as failures of inhibition. Indeed, framed broadly one can find inhibition in most cognitive control problems, because they always involve some irrelevant or inappropriate response or thought that needs to be avoided, suppressed, or overcome. This is the case in the laboratory, as well. Interference on the Stroop task might arise from an inability to inhibit the habit of word reading. Larger task-switching costs are due to a failure of inhibiting the prior, competing task set. Hierarchical control requires restraining certain subtasks or rule sets in favor of new ones.

Importantly, however, the term inhibition can be misleading. As the history of the concept makes clear, it has taken on different senses and different roles. This ambiguity has led to confusion and problems in trying to pin down an inhibitory process as a distinct control mechanism in the brain, so it is worth clarifying what we mean by the term and what our minimum qualifications might be for a true inhibitory control process.

First, the use of the word inhibition as a suppressive force in behavior or cognition should not be confused with the neural inhibition discussed in earlier chapters. Neural inhibition refers to neuronal interactions that reduce the likelihood that a neuron will fire. This could plausibly be a mechanism for behavioral or cognitive inhibition, but it does not have to be. For example, the corticostriatal mechanisms of gating we met in chapters 3 and 4 use inhibitory neurons stacked in a disinhibitory chain to select a behavior, rather than to suppress it. Thus, in this case, neural inhibition paradoxically amplifies and expresses action rather than suppressing it.

Conversely, excitation at the neural level can result in inhibition in behavior by simply selecting another behavior or just making a neural code noisier. If I want to inhibit the sound of people talking outside my office, I need only turn on some music in my office to drown them out. This is an excitatory means of inhibition that could express in neurons, as well. Indeed, though the prefrontal cortex is necessary for inhibition of behavior, the neural projections out of the prefrontal are excitatory, not inhibitory.

Second, the distinction between inhibition the consequence and inhibition the process should not be blurred. Just because we observe a behavior to be inhibited, this does not always mean there is an inhibitory process behind the stoppage. For example, if I choose to head home from work around 5 p.m., I can expect my overall travel time to be greatly slowed relative to that of an off-peak hour. So, strictly speaking, my travel home has been inhibited. Of course, this slowing is not due to any specific inhibitory influence on my car. There is no maniacal associate dean at Brown University who emerges at twilight to put winches on faculty cars and drag us back to the office. Rather, I am slowed because it is rush hour, and other people are heading home at the same time. The road has a carrying capacity, the cars exceed it, and I have to wait for everyone to file through. Thus, my ride is inhibited without a direct process of inhibition.

Similarly, slowing or stopping of a thought or a behavior does not, on its own, require the action of an inhibitory process in the brain. Inhibited behavior can instead reflect a failure to initiate that behavior in the first place, or cross-talk and interference during processing. And again, akin to James’s supplanting mechanism of inhibition, the inhibition of our behavior could be the result of a cognitive process that selects appropriate things rather than inhibits inappropriate ones. Thus, we can explain many of the impulsive or incorrect behaviors without citing a failed inhibitory mechanism. Indeed, in their broad, integrative account of the functions disrupted by frontal dysexecutive syndrome, leading neuropsychologists of executive function Don Stuss and Mick Alexander explicitly left inhibitory control out of the picture.[12] They claimed that most, if not all, supposedly inhibitory deficits that follow frontal lobe damage could be explained in terms of deficits in monitoring, motivation, or task setting, not inhibition.

Third, verifiable instances of suppression in the brain do not require a central inhibitory control process but can emerge as a side effect of what we have chosen. There are many examples in the brain of a mechanism called lateral inhibition, whereby the activation of a certain population of cells inhibits another nearby population of cells, typically to the degree that they compete for processing. So, for example, if two cells fire to objects in the same spot in space but in different orientations, one cell may be suppressed to the degree that the other is active.[13] Indeed, this interaction was the basis of the biased competition model of attention discussed in chapter 5.[14] Attending to one location suppresses the other to the degree that they are in competition.

Isn’t lateral inhibition an inhibitory control mechanism, then? Yes and no. It is inhibitory in the sense that neural coding can be suppressed when it is a competitor, and this mechanism makes that neural representation harder to activate or that response pathway harder to follow. However, the central source of this inhibition was actually an excitatory signal. The choice of what to inhibit was the local side effect of choosing the relevant population, not suppressing the irrelevant one. Thus, we may still want more from a central inhibitory control process. To say that we have inhibitory control, we need evidence of a central mechanism that suppresses active neural representations in a goal-directed way.

We have already talked about one such inhibitory system in the brain, namely, the NoGo pathway. As a reminder, firing of the Go pathway within the striatum disinhibits corticothalamic drive, thereby selecting a response or a working memory representation for control. Firing of the NoGo pathway, by contrast, serves to deepen the neural inhibition of thalamocortical drive. As a consequence, firing of this pathway makes disinhibition by the Go pathway harder, and so it effectively restrains the amplification of a specific pending response or context representation in working memory. This dynamic qualifies as true inhibition by the NoGo pathway. It is not selecting an alternative course of action; rather, it is restraining the Go pathway from acting on a specific movement or working memory representation.

Importantly, however, there is another central process of inhibition, also tied to the basal ganglia, that we have not discussed so far. This inhibitory process exerts its suppressive influence directly. Indeed, rather than merely preventing the activation of a specific cortical representation like the NoGo pathway, this system of inhibition can rapidly suppress an already activated representation. This is the brain’s “stopping system,” and its story begins with a simple act of control.

Racing to Stop

Over his career, Vanderbilt psychologist Gordon Logan has made many fundamental contributions to our understanding of cognitive control. And, arguably better than anyone else, Logan has sought to understand cognitive control in terms of its verifiable acts, like inhibition. This approach has made it clear how challenging studying control can be. Logan once likened the problem to catching a fish in a stream by hand. You can just make out the fish—a shimmer and a glint below the water’s surface—but when you try to grab it, the fish is not there. Cognitive control is similar. Just when you think you have it in your sights at last, you reach out and find it gone.

As we have seen in this chapter, inhibition has long been one of these fish. Behaviors that appear inhibitory turn out to arise from mechanisms that are not inhibitory at all. So, how do we test inhibition in a way that can’t be explained as failing to initiate an action or selecting something else? How do we measure inhibition? In a line of work beginning with a seminal paper in 1984, Gordon Logan sought to address these problems directly using a task called the stop-signal procedure.[15]

In the basic stop-signal task, the research participant faces two kinds of signals, a “go” stimulus and a “stop” stimulus. Go stimuli appear on every trial of the experiment and cue the research volunteer to make a specific movement as quickly as possible. For example, the go stimulus might be an arrow pointing left or right. Thus, on every trial, they would press with their left hand to a leftward arrow, and their right to a rightward one.

Importantly, there are occasional, unpredictable trials in which the go stimulus is followed after a brief moment by a stop stimulus. The stop stimulus, which might be a tone for example, tells the research participant to cancel whatever response they were making to the go stimulus. In other words, on every trial, participants will initiate a rapid response to the go stimulus. But, when a stop stimulus appears, they will need to rescind this response in some way. They have to stop.

This task provides an elegant means of testing true inhibitory control. First, inhibition of the go response is hard to explain as the indirect consequence of selecting something else or a failure to initiate a response. There is no other response to be selected. Rather, the response to the stop signal is to countermand the response already chosen and initiated. Second, the go signal occurs in every trial, and the stop signal happens after the go signal, so there is no question of whether people inhibit versus make the decision not to go at all.

When people successfully inhibit in this task, they do not respond at all. So, how can we measure that process? How does one measure the speed with which something did not happen? The answer to this requires elaborating the task a bit more.

Recall that the stop signal occurs after the go signal. The gap in time between the presentation of the go and stop signals is referred to as the stop-signal delay. When the stop-signal delay is very short, say between 50 and 200 milliseconds, people have an easier time stopping their response. But, when the delay is longer, people are more likely to make errors by responding when they should have stopped. In these cases, they are like Fred Brown passing the ball to James Worthy: they know the action is wrong, but it has gone too far down the path to execution for them to stop.

Logan and Cowan showed that this pattern of stopping across different delays between go and stop signals is like a horse race between the motor command and the inhibitory stop signal that countermands it. If the motor command wins the race, then you make the movement. If the stop signal wins the race, you do not. The stop-signal delay determines how much of a leadoff is being given to the motor command at the start of the race. If the gap is long enough, then the stop signal can’t catch up.

Expressed this way, we now have a handy means of measuring the speed of the inhibitory process. To illustrate, imagine that I am in a 100-meter dash with Olympic champion and world record holder Usain Bolt. In this example, I represent the slower motor command, and Usain represents the fast stop signal.

Just as we want to measure stopping time, I want to measure Bolt’s time to complete the 100-yard dash, but I don’t have a stopwatch handy. However, let’s say that I do have a starting gun with a timer that I can set to whatever delay I like before it fires. We agree that Usain will give me a head start in the race by setting a delay on the starting gun. We will start the timer on the gun when I start running, and then he will start running when the gun fires. We find that when I am given a 6.42-second head start, Usain and I consistently tie at the finish. I know from prior races that I run the 100-meter dash in 16 seconds, so I simply subtract that 6.42-s start delay from my 16s time and voila, Bolt’s time to finish the race must been 9.58 seconds. (Of course, this scenario is not realistic. As a good experimentalist, I would never be caught without a stopwatch of some sort.)

We can use a similar logic to estimate the speed of the inhibitory process. We measure the time it takes to respond when you receive only the go signal. This tells us how fast the motor command will run the race. We then make the stop-signal delay longer and shorter until we reach a point at which you fail to stop about half the time. This is essentially the point at which we’ve given enough of a leadoff to the motor command that it ties the faster stop signal on every race.

Now, we can use the same basic arithmetic to estimate the speed of inhibition. If we subtract the stop-signal delay from the motor-response time, the difference will be the time it takes to inhibit the motor response. We call this time the stop-signal reaction time, and it is a measurement of the strength of inhibitory control. In other words, the faster one’s stop-signal reaction time, the stronger one’s inhibition process.

Importantly, however, this approach to measuring inhibition depends on the logic of the horse race, and that logic assumes that the racers are independent of each other, that is, racing in separate lanes. If stop signals get faster or slower depending on the type of motor command, or if the racers occupy the same lane and interfere with each other, then these measurements will be systematically off. In their studies, Logan and Cowan presented evidence that people’s behavior in this task mostly follows a pattern one would expect if there were independence between stopping and going. Furthermore, evidence from the brain indicates that fast stopping of this kind may indeed be enacted by a corticobasal ganglia pathway distinct from the corticostriatal pathway used for “going.”

Stopping depends on the distinct frontal-basal ganglia network shown in Figure 6.1.[16] When people perform a stop signal task during fMRI scanning, there is consistent activation in this network on stop but not on go trials.[17] Moreover, people who show greater activity in this network also tend to stop faster. In contrast, when people make a response to the go stimulus, activity is observed in the corticostriatal pathway we have been discussing to this point, which is a distinct network both in the prefrontal cortex and subcortically. Thus, whereas selective gating relies on corticostriatal loops, stopping is executed through a distinct network.

FIGURE 6.1. Schematic showing the loop between cortex (motor and premotor cortex and right VLPFC) and subcortical structures: the subthalamic nucleus (STN), the globus pallidus (GPi), and the motor thalamus (motor Thal.). PreSMA is also involved in this network. Adapted from Aron et al. (2016), Figure 1A.

How does this pathway induce stopping? Connections from the right ventrolateral prefrontal cortex (R VLPFC) and dorsomedial prefrontal cortex descend to a subcortical structure called the subthalamic nucleus. These pathways presumably transmit some contextual information about the conditions for stopping. Excitatory neurons in the subthalamic nucleus then project to the globus pallidus. You may recall from chapter 3 that the globus pallidus inhibits thalamocortical drive and prevents movement, so this pathway through the subthalamic nucleus can influence the globus pallidus in only a couple synapses and rapidly clamp down on cortical activity for a response. Doing so bypasses the striatum and the slower deliberations going on between Go and NoGo cells. This means even if the Go pathway has decided to gate out an action, such as a left-hand response to an arrow, this fast pathway through the subthalamic nucleus can rapidly slam on the brakes before this happens.

Though not without controversy, the evidence for this model of fast inhibitory control through the subthalamic nucleus is some of the strongest in the field, much of it owing to the rigorous and elegant work of scientists like Trevor Robbins at the University of Cambridge and cognitive neuroscientist Adam Aron at the University of California San Diego (UCSD) and their collaborators.

First, there is evidence that each of the structures in this network is necessary for stopping. In an early report, Aron and Robbins observed that the amount of damage to the right ventrolateral prefrontal cortex in stroke patients negatively correlated with their strength of stopping, as measured by stop-signal reaction time.[18] Importantly, these stopping deficits did not follow from damage to regions of the frontoparietal control system we associate with selective gating. This dissociation again points to separate control functions served by the frontoparietal control system we’ve discussed to this point and this inhibitory network.[19]

Moreover, this inhibitory network is, indeed, a network in that these regions work together to enact fast stopping. The cortical members of the network and the subthalamic nucleus are connected by direct white-matter tracts, allowing for fast communication between them, and the integrity of these connections correlates with the strength of stopping.[20] Further, studies of stopping in both humans and animals have found a signature increase in low-frequency neural oscillations among these regions that is closely tied to the onset of inhibition.[21] In other words, just before people stop their responses, chatter increases in this network and on a signature bandwidth, much like tuning in a particular channel on a shortwave radio.

Interestingly, chatter in the brain also increases on this signature bandwidth when people are surprised.[22] We have all been surprised by an unexpected event. A door suddenly slams, someone puts a hand on our shoulder from behind, a dog starts barking behind a fence we are passing, or a deer crosses the road in front of our car. Some of these events demand a fast change in our behavior, like the deer; others might not, like the door slamming. But what these events have in common is that they require us to stop momentarily and judge whether circumstances have changed and if we need to pursue a new course of action. Thus, when a surprising event occurs, we pause. It may be for only a moment, but action stops. In several studies, Aron and Jan Wessel found that surprise engages the same network that included the subthalamic nucleus and uses the same oscillatory neural signature just before the pause.[23]

At the heart of this circuit for stopping is the subthalamic nucleus.[24] This area of the brain is a cluster of neurons sitting just below the thalamus, and through its influence on the globus pallidus, it has been thought critical for stopping. However, the evidence for this was mostly circumstantial until recently. To conclusively demonstrate its necessity for stopping, one must do the kind of circuit cracking that is possible only in animal models. Recently, Kathryn Fife, Thomas Hnasko, and other collaborators at UCSD, including Adam Aron, provided this specific evidence.[25] They tested the necessity of the subthalamic nucleus for stopping using a technique called optogenetics.

Optogenetic techniques are currently revolutionizing neuroscience. They allow scientists to stimulate specific circuits using particular wavelengths of light. The basic procedures require some genetic wizardry to generate neurons that have light-sensitive ion channels, which will respond when light of a specific wavelength is cast on them. Depending on the channel, this will stimulate the cell to fire or will suppress it. The advantage of this method over other kinds of stimulation, like electrical stimulation, is that optogenetics permits a high degree of specificity. Because neurons don’t naturally respond to light in a mouse brain, shining a light on a particular patch of cortex will excite only the cells genetically modified to do so. This is a powerful way to determine how different cells are contributing to the computations of an entire circuit.

Fife and her colleagues took this approach to test the subthalamic nucleus of mice during stopping. Mice in her experiment would lick at a spout to receive some strawberry milk. The bouts of licking could be measured precisely in terms of the number and frequency of licks. Using optogenetic stimulation, the cells in the subthalamic nucleus were then excited with a laser. Whenever this happened, the licking bout stopped. Thus, stimulating the subthalamic nucleus caused inhibition of the motor response. But this is only half the logic. Perhaps this stimulation just caused a craving for Cheerios that blocked a desire for the milk. We need evidence that without the subthalamic nucleus there won’t be a stop given the right context.

To address this problem, the scientists took advantage of the involvement of the stopping network in surprise. If an unexpected sound is played while a mouse is licking, it will pause. As we noted earlier, this pause to an unexpected event likely requires that route through the subthalamic nucleus. So, optogenetic stimulation was used to inhibit the subthalamic nucleus rather than excite it during a surprising event. When the laser was on, the unexpected sound no longer induced the same pause in licking. In other words, by taking the subthalamic nucleus offline, a fast stop to a surprising event did not occur. This provides strong evidence that the subthalamic nucleus causes us to stop.

Another important feature of the stopping signal through the subthalamic nucleus is that it is global, affecting all movements. In chapter 4, we discussed experiments where transcranial magnetic stimulation (TMS) was used to safely stimulate the brain and perturb cortical activity during a sequential task. However, TMS can also be used as a stimulus to induce a response by stimulating neurons. For example, if we pulse TMS over motor cortex, we will elicit a movement, which we can measure as an electrical potential in the muscle. This stimulation can be relatively precise. By stimulating the correct part of the motor cortex, we can induce individual fingers to move, for example.

Even if there is no overt movement, scientists can measure the electrical impulse generated in the muscle, called a motor potential. These motor potentials reflect the propagation of neural activity down the corticospinal tract. Interestingly, if I pulse your motor cortex while you are preparing to move your finger, but not actually moving it, the TMS pulse will yield a bigger motor potential than if you weren’t preparing a movement. This is because preparing a movement makes your motor cortex more excitable, and so the same TMS stimulation will yield a bigger output. Scientists can take advantage of this phenomenon and use TMS-evoked motor potentials as a measure of how excitable the motor cortex is at any given moment. The bigger the motor potential, the more excitable the cortex must be. And likewise, a potential smaller than a control baseline is evidence that the cortex has been inhibited.

Aron and colleagues used this approach to test whether the inhibition induced by the stopping network is global or specific.[26] They designed a version of the stop signal task in which people had to stop different muscle groups, depending on the task. Thus, the go stimulus might require an eye movement in one version of the task and a leg movement or a hand movement in another. As usual, stop trials required stopping these movements.

On stop trials, Aron and his colleagues used TMS to stimulate a different part of the motor cortex than was being stopped. For example, if someone was making eye movements, TMS would stimulate the hand area of motor cortex and measure the muscle potential in the hand. Thus, any effect of stopping on cortical excitability could not be explained in terms of the goals of the task. If the task did not require moving the hand, then it also did not require suppressing the hand part of motor cortex.

Nevertheless, Aron and colleagues found that when people had to stop, it didn’t matter which specific muscle group was being stopped; all motor potentials were suppressed. In other words, if they were stopping an eye movement, a TMS pulse to the hand area of motor cortex evoked a reduced motor potential in the muscles of the hand. This means that the cortical area devoted to hand movements was suppressed, even though there was no demand either to move or to stop the hand. The hand was suppressed as a side effect of the eye movement being stopped.

Stopping through the subthalamic nucleus is not only fast, it is also global. Beyond the speed with which control is implemented, this global scope of effect distinguishes this inhibitory network from the NoGo pathway through the striatum used for gating. The mappings of connections between cortex and the striatum and back to thalamus that make up the Go and NoGo pathways are highly specific. This allows particular cortical ensembles to be selectively gated or not gated, but this process is also slower. When one needs to favor speed over specificity, such as stopping before stepping into the street in front of an oncoming car, the fast but global stop enacted by the subthalamic nucleus pathway is essential.

In summary, then, a specific prefrontal cortical-subthalamic nucleus network is crucial for fast and global inhibition of movements. The actions of this network are accurately described as a central act of inhibitory control. It is a goal-directed, suppressive process that results in withholding of action. However, these acts of inhibition are all in the motor domain, stopping movements. A crucial remaining question is whether this inhibitory control extends to thought, as well.

Stopping Cognition

Can we inhibit a thought? Thoughts can certainly be distractions in and of themselves, and sometimes, it surely would be nice to inhibit them. We can find ourselves thinking about what we’d like to do later, rather than what we are doing right now. We can ruminate for a bit too long over the bumps in our lives. Sometimes, small events occupy more time than they should. We might find ourselves returning to that offhand remark someone made at a party or a negative experience we had at work. We can turn events over and over in our minds, until at some point they either lose their emotional flavor, or we are simply able to reappraise them or push them back and move on.

Some people are not able to move on in this way. For these people, unwanted thoughts can become intrusive and even lead to mental health problems. For example, patients with obsessive-compulsive disorder, or OCD, might repeatedly feel they have forgotten to lock the door of their house or feel sure they unknowingly hit a pedestrian on their drive to work. Even after patients with OCD repeatedly check and recheck the door or redrive their route to work, these intrusive thoughts return to haunt them continuously. They are seemingly unable to stop the thoughts from arising or to stem their persistence.

As another example, people who have experienced traumatic events will sometimes develop posttraumatic stress disorder, or PTSD. A feature of PTSD is the intrusion of unwanted memories from the traumatic experience, which come to mind unbidden and cause considerable emotional pain and distress. PTSD has been linked to depression and suicide. These individuals are unable to control when and how these memories come to mind or the effect the memories have once they do arise. In his book The Seven Sins of Memory, memory expert Daniel Schacter has described these intrusive memories as an extreme case of the “sin of persistence,” an inability to rid ourselves of those memories we wish to forget.[27]

Cognitive control mechanisms of inhibition may be among the defenses we have against these intrusions taking over and dominating our thoughts and lives. But can we use inhibition to control our thoughts, such as by keeping them out of mind or suppressing them before they come to occupy our working memory?

Of course, as we have discussed, in many situations, inhibition of these intrusive thoughts or memories is an outcome, but it may not be the consequence of an inhibitory mechanism. Consider the classic challenge from social psychologist Daniel Wegner to “not think about a white bear.”[28] It is very hard to suppress the thought of a white bear. In fact, as soon as we hear this command, the one thing that will almost surely come to mind is that impertinent bear, likely with a smug grin on its furry face. But we can succeed in not thinking about it, at least momentarily, such as by actively thinking of everything else.

Thus, the systems of planning and working memory gating discussed so far in the book are more than capable of dealing with cases of avoiding distracting thoughts by privileging task-relevant information and goals in working memory instead. Selecting these can supplant other cognition clogging up our mental works. Nevertheless, recent evidence suggests that the fast inhibitory system involving the subthalamic nucleus can affect more than just simple motor responding. It can also affect the way we think and make decisions.

Consider a difficult decision you might need to make under a bit of time pressure. For example, you are out to dinner with friends, and the waitperson comes and asks for your order. Now, imagine that this menu has a selection of yawners, none of which particularly jumps out at you. Or, worse, there are two dishes that look intriguing, but you can pick only one. The waitperson stares at you expectantly, pen poised over notepad. You look from item to item wishing the words on the page would somehow make the decision for you.

What do you do? Often, in a situation like this, you ask the server to take others’ orders and come back to you. You give yourself more time to make the decision. It’s not the case that some new information will magically appear on the menu during this pause. Nevertheless, delaying can actually be a very adaptive choice in a situation like this because moderately delaying hard choices is a pretty good strategy for making better decisions when you are uncertain.

Psychologists refer to this strategy as the speed-accuracy tradeoff. By giving ourselves more time to make a decision, we allow more time for evidence to accumulate. We are less likely to be pulled by quick or impulsive influences. We have collected more data, in some sense.

Adding unnecessary time is costly, of course, and so ideally, we can adjust how long we take to make a decision based on the difficulty of the decision itself. For example, I don’t need to spend very long deciding whether to have the scallops or the fried brain sandwich. For me, the evidence favoring scallops is overwhelming, and a quick decision will suffice. But if I have comparable options, like scallops versus swordfish, I want to give myself longer to make this decision.

How might the brain enact this kind of control? Michael Frank and I collaborated on a project to investigate the neural systems that support slowing during difficult decisions.[29] Our research participants were shown a series of abstract images. Each image corresponded to a particular key press that would receive points 85% of the time. The participants’ job was to figure out which key press was the most likely to win points depending on the individual image and the feedback they received after responding. Importantly, in the process of learning these key presses, the participants would be faced with a series of decisions about which key to press. Early on, when they knew little about the values associated with each response, these decisions would be highly uncertain. As the participants learned more, their decisions would become less uncertain, though this uncertainty would still fluctuate. So, based on the particular history of each person’s experience with the task, we could estimate how uncertain each of their decisions was and could assign a number to it. We found that when choices were more uncertain, people slowed down because their threshold for making the decision had been raised. They were adapting their decisions based on how uncertain they were—an act of control.

We next tested how the brain responds to these increases in uncertainty. To do so, we simultaneously recorded from their brains using fMRI and electroencephalography to measure both where and when changes in neural signals occurred. We found that increases in uncertainty during a decision were associated with greater low-frequency oscillatory communication between the medial prefrontal cortex and the subthalamic nucleus. The increased subthalamic nucleus activity, in turn, correlated with people slowing down and making more conservative decisions. In effect, uncertain decisions drove the subthalamic nucleus to put on the brakes. By exciting its targets in the globus pallidus, the subthalamic nucleus tamped down on thalamocortical drive and put the kibosh on any response. As a result, more evidence was required for any response to be made.

Of course, these correlations from neuroimaging do not provide evidence that the subthalamic nucleus is actually causing slowing under uncertainty, the way it does during stopping. However, other studies have provided this evidence. This work focused on a subset of patients with advanced Parkinson’s disease receiving a treatment called “deep brain stimulation.”

Deep brain stimulation is an effective treatment for late-stage Parkinson’s disease. It requires brain surgery to implant the stimulator, and so it is often used only when dopamine medications are becoming less consistently effective. Nevertheless, when combined with medication, patients receiving deep brain stimulation exhibit substantial reductions in their symptoms, particularly those related to tremor, slowness, and rigidity. Importantly, the brain target of the stimulation is none other than the subthalamic nucleus. Though its exact action is still a matter of research, the stimulation likely has the effect of disrupting the subthalamic nucleus and so reducing its inhibitory influence on responding. Thus, by testing patients while their stimulators are on versus off, one can test the role of the subthalamic nucleus in tasks like those involving decision making.

In a series of experiments, James Cavanagh, Michael Frank, and their collaborators observed that when patients’ stimulators are turned on, they behave as though they make quicker, less conservative decisions.[30] As a consequence, when the patients face hard, high-conflict choices they respond too quickly and often incorrectly.

These results support the hypothesis that inhibition through the stopping system can affect decision making by forcing slowing and thus raising the threshold of evidence needed for a decision.

Is this really stopping cognition? One might reasonably argue that the preceding example could still be entirely about stopping movement. In effect, the stopping system puts the brakes on the actual response, which has the side effect of allowing more time for thought and deliberation, but there is still no direct suppression of a particular thought or memory.

The evidence is less rich for inhibition of thoughts, partly because of its challenges. Nonetheless, an impressive and innovative line of experiments by cognitive neuroscientist Michael Anderson is probably the best evidence we have for inhibition of cognition. His focus has been on how we voluntarily suppress a memory.

Over the course of his career, Anderson has developed an ingenious experimental paradigm for memory suppression centered on the think/no think task.[31] The think/no-think task is somewhat like a stop-signal task, except that the action being stopped is an act of memory retrieval rather than a movement. In the basic experiment, volunteers start with a study phase during which they are provided with a large set of word pairs to associate with each other. So, they might see arbitrary pairs like “tuna–chair,” “nail–berry,” and so forth.

A subset of these pairs is then chosen for the next part of the experiment, called the think/no-think phase. In this phase, one of the words in the pair is shown as a cue, but not the other, like “tuna–?” And this is where it gets tricky. If the cue word is colored green, people have to remember the other word in the pair, “chair” in this case. However, if the word is red, they must not think of the associate. In other words, when they see “tuna” colored red, they need to suppress the memory of chair. In a task like this, retrieval of the word paired with the cue can leap to mind whether they want to retrieve it or not. So, if they get the no-think instruction, they must somehow suppress this retrieval and not think about the associate.

After they finish the think/no-think phase, the participants are again given cues from all the pairs studied in the first phase. They are asked to report the words that were paired with each cue. So, there are three basic groups of cues: cues from the pairs in the “think” condition, cues from the “no-think” condition, and finally, cues from pairs that were not seen at all in the think/no-think phase. How does people’s memory differ across these three groups?

First, people’s memory for the words pairs in the no-think condition is worse than the pairs from the think condition. This is somewhat interesting, but it is not the strongest evidence for suppression yet. For example, perhaps memory for the no-think pairs isn’t suppressed so much as memory for the think pairs is enhanced. After all, people were allowed to actively practice those pairs. Maybe this is just evidence that practice makes memory better.

As a control for practice, we have the word pairs that were seen during initial study but then not seen at all during the think/no-think phase. These pairs were unpracticed, but they were also not subject to no-think suppression. Strikingly, people’s memory for the no-think pairs is worse than for the unpracticed pairs! In other words, they apparently suppress the memory of the no-think pairs to the point that they are harder to retrieve subsequently. As a result, they forgot the no-think pairs at a higher rate than if they hadn’t seen that pair at all. Maybe Freud wasn’t high on cocaine after all. (He was.)

Using the think/no-think task, Anderson has investigated the neural systems that are active when people suppress retrieval. These experiments have exposed substantial similarity between the networks associated with retrieval suppression and those required for fast stopping of a movement,[32] particularly in the prefrontal cortex. Thus, this is some circumstantial evidence that our fast, global stopping system is engaged during cognitive inhibition.

However, there are also some notable differences. First, these studies of memory suppression have not consistently observed activity in the subthalamic nucleus. It is unclear why this central player in the motor-stopping circuit is conspicuously absent from memory suppression. But one should be cautious about overinterpreting its absence. There’s a saying among scientists that the absence of evidence is never evidence of absence. Consider that a signal from a deep brain structure like the subthalamic nucleus is harder to measure under ideal conditions, let alone when one is testing a subtle process like memory suppression. Thus, it may be active during memory suppression, but we just can’t detect it.

This caveat notwithstanding, another possibility is that the subthalamic nucleus is not required for this kind of nonmotor stopping. Indeed, the neuroimaging evidence suggests that the consequence of memory inhibition is suppression of the hippocampus rather than of motor cortices. Recall from chapter 2 that the hippocampus is the structure in the middle of the temporal lobes necessary for both encoding and retrieval of long-term memories, so it is possible that when people try not to remember, they suppress the retrieval system itself. Suppression of a memory, as opposed to suppression of a motor response, might require distinct pathways through the basal ganglia. However, the prefrontal cortical areas needed to monitor for the circumstances of stopping are the same as in the movement case.

These are interesting ideas about which to speculate, but much more work is needed to understand the mechanisms underlying this kind of suppression. Anderson is certainly well down that path, and others have started to look closer, as well. Notably, Aron has recently reproduced some of Anderson’s observations using a gold-standard mechanism for scientific replication called a registered report. In this study, he also located the same signature low-frequency oscillation during memory suppression that is observed during motor stopping, which draws an even closer link between these functions and their underlying brain network.[33]

Thus, this line of work on memory inhibition offers the strongest evidence to date of active cognitive inhibition by a central inhibitory mechanism. It is tempting to speculate that this mechanism for memory suppression may extend to the more general control of intrusive thoughts. Though we can sometimes avoid unwanted thoughts by thinking of other matters or staying focused on a task at hand, it is inevitable that intrusions will spring to mind from time to time. These intrusions, whether arising from the world around us or thought itself, can sometimes lead to memories or thoughts we want to avoid. In this case, an inhibitory mechanism like that used for memory suppression might be among the cognitive tools we can use to prevent that cycle. This may be a mechanism of control for a brain with a powerful system of episodic future thought like ours.

Stopping Ourselves

A pillar of human nature is our ability to inhibit our drives, overcome the forces of habit, and consequently to avoid behaving in ways that act counter to our goals and circumstances. There can be little doubt that we can do so, and seemingly voluntarily, by some force of will. Yet, even though our behavior or even our thoughts are inhibited, an inhibitory process does not necessarily deserve the credit. We have seen that multiple control processes achieve inhibition in our thoughts and action; some can be characterized as inhibition, and some really not.

The fast stopping system is a clear case of a central inhibitory control system that can suppress behavior. This network between frontal cortex and subthalamic nucleus joins the corticostriatal gating system to support cognitive control. There are some striking similarities among these systems. They both involve more elaborated cortical systems plugging into ancient structures within the basal ganglia that have close ties to basic motor control. Indeed, just as selective gating allowed for consideration of movements without doing them, the ability to quickly stop an action was likely important to survival almost immediately. As soon as an animal could walk, it could also walk off a cliff. Just as with the selective gating system, however, our stopping system was elaborated over the course of evolution to stop in more complex settings and potentially to stop more abstract thoughts and actions.

Importantly, however, the selective gating and global stopping systems are distinct. Both in cortex and in the basal ganglia itself, the corticostriatal gating system runs in parallel to the faster global stopping system. This division of labor is adaptive, too, particularly if the circumstances for stopping action might need to be monitored separately from those influencing what action to take. This setup likewise assigns the advantage of speed for global stopping and the advantage of precision for going. In other words, it’s better not to do anything than impulsively do one thing you shouldn’t.

The multifaceted nature of inhibition also makes more concrete the challenges of understanding the relationship between cognitive control and the problems people experience in their own lives. Take, as an example, an impulsive behavior such as someone grabbing the last doughnut without considering the impact on their own waistline. One account of this would be a failure of stopping, as with the patients with Parkinson’s undergoing deep brain stimulation. Maybe this person is aware of the conflict in this decision, such as the value of the doughnut versus the value of one’s self image, but is unable to use this to regulate themselves and wait to make the decision.

However, this is not the only explanation of that particular impulsive behavior. Another possibility is that the gating system is not properly assigning value to different outcomes and so is making incorrect choices.

In fact, this latter form of impulsivity was also evident in the patients with Parkinson’s we discussed previously. In one experiment, the patients were tested not only on or off their stimulation, but also on or off their dopamine medications.[34]. Recall that dopamine levels can affect the Go and NoGo gating pathways. The patients on their medications had higher levels of dopamine, which disrupted the NoGo system and made it hard for them to learn from bad outcomes. The result was that these patients failed to avoid options that led to these bad outcomes. In other words, these patients would make impulsive responses, but this had nothing to do with their threshold for the decision or the stopping pathway. Rather, it was how they were valuing the various options leading to a response.

This complexity has profound implications for understanding mental health and disease. Consider the case of addiction. Addicts form a habit, be it dependency on a substance or a particular behavior like gambling or sex, which they are subsequently unable to control. They are compelled to engage in that activity and do so even at great cost to their own health, family, and lives.

Trevor Robbins is among the foremost scientists in the world studying the relationship between addiction and the neurochemical bases of cognitive control in the brain. Robbins with Jeffrey Dalley cites a range of evidence that people who are impulsive show a higher likelihood of forming addictions.[35] But he also notes considerable heterogeneity in the underlying causes of this impulsivity and its relationship to inhibitory control.

For example, addicts who abuse stimulants, like cocaine or amphetamine, tend to also have slower stop-signal reaction times relative to nonabusing controls. Interestingly, the first-degree relatives of stimulant abusers, who do not use drugs themselves, have the same slowed stop-signal reaction time. This observation indicates that the slowed stopping among the addicts is not entirely attributable to their drug use, and further, inhibition in this group is a trait that puts one at risk for forming an addiction, but doesn’t guarantee it. Put in a more positive way, an effective stopping system may be a check against one type of impulsivity that leads to addiction.

However, other dimensions to impulsivity also appear related to addiction. One example is sensation seeking. People who have this personality trait desire varied, usually intense, experiences and feelings. Sensation seekers are more likely to experiment with drugs and alcohol and engage in other risky behaviors. And these individuals also tend to be more impulsive.

Yet, the degree of sensation seeking across people is not correlated with brain measures of the stopping network. They differ, rather, in the thickness of the cortex in areas of the frontoparietal system associated with working memory and selective gating. Thus, rather than having a problem setting thresholds to make difficult decisions, sensation seekers making the decision to abuse drugs may differently value various actions, like taking cocaine, used to make choices. As a result, they gate impulsive behaviors that lead to addiction. This outcome is similar to that of individuals with weak stopping control, but the cause and mechanism are distinct.

To conclude, the concept of inhibition has been at once a mainstay of our view of human nature and also a source of consistent controversy in both neuroscience and psychology. Yet the evidence in the last several years has indicated that we can and do directly exert inhibitory influences on our actions and possibly our thoughts. Indeed, we likely have more than one means of doing so, and so our successes and failures of inhibition can arise from different sources—one of which is that of value or motivation. The next chapter will expand on this theme of value and look more closely at how control can be understood in motivational terms.

CHAPTER 7

The Costs and Benefits of Control

On April 25, 1940, a 14-year-old girl named Elsie Nicks was admitted to the Radcliffe Infirmary in Oxford exhibiting symptoms of a rare and peculiar nature.[1] This was her second visit to the historic clinic. A month earlier, she had been seen by doctors there for persistent and severe headaches. An X-ray had revealed a mass growing in her third ventricle, a fluid-filled space in the middle of the brain. On that prior visit, it was decided not to operate, and the headaches subsided with rest.

Elsie’s visit on this second occasion was different. Following another attack of severe headaches, she had fallen into an unusual and unresponsive state. When doctors had seen her the previous month, she had been an active young girl, interacting with them in the usual way a child does in a clinic. Now, it was as though a different person was before them. She did not speak or move. She was content to sit silently in one position, yet she was not truly catatonic or unconscious. She was aware. Her eyes would follow people and objects around the room. If she was asked to do something repeatedly, she would eventually comply, though slowly and without vigor. She could answer questions but did so in a whisper and typically with only the merest one- or two-word utterances.

Even items and situations that would enliven most children inspired little in her. For example, when she was handed a piece of chocolate, she made a motion to eat it, but it fell out of her hand, and she had no interest in picking it up. If she was tickled, she gave a feeble smile and turned her head away. She was as indifferent to sugar placed in her mouth as to bitter quinine. Even a pinprick was met with only a slight grimace and a slow movement of her hands toward the source of the pain. In sum, Elsie could move and act, but she had seemingly lost the desire to do so.

Thankfully, the story of Elsie in the medical literature has a happy ending. When the cyst in Elsie’s brain was removed surgically, her symptoms and headaches disappeared. She returned to her previous engaged self. And as of the publication of her case in 1941, the symptoms had not returned.

Elsie Nicks was the first reported case of a condition called akinetic mutism. Though it can stem from different causes, this condition is often associated with damage to the medial prefrontal cortex, particularly a cortical area called the anterior cingulate cortex. Neurologists recognize akinetic mutism as an unresponsive state along with an extreme reluctance to perform even simple movements. In other words, this is primarily a deficit of motivation, and it illustrates that getting things done is not just about the mechanisms for choosing actions; it also concerns the desires, incentives, and benefits we attain when we do so.

Think about your own moment-to-moment behavior. Perhaps you are reading a book. You set it down to take a sip of tea. You decide the tea needs a bit more heat, so you head to the kitchen to pop it in the microwave for a few moments. While waiting, you check social media on your smartphone. You perform each of these actions because, at some level, you want to do so. You are invigorated to action because of a desired outcome, be it the pleasure of reading or a better-tasting drink. You could have decided that the kitchen is too far away to merit going all the way there to heat up your tea. Or you might be at a particularly compelling point in your book and so don’t want to stop reading, cold tea be damned. The point should be obvious. We don’t do things just because of habits or gating mechanisms or stimulus-response associations. We often get things done because we want to do them. Cognitive control is motivated.

When viewed through a motivational frame, many control problems we’ve considered to this point take on a new dimension. Consider multitasking, for example. From a motivational perspective, we can reframe multitasking as a sort of foraging problem. When a berry-eating animal forages, it will spend some time at a bush full of berries before moving to another. How does it decide when it is time to move to a new bush? When the animal arrives, the bush is brimming with plump, juicy berries. But as the animal gobbles up the berries, there are fewer and fewer berries to be harvested. Of course, there are probably other bushes in the area, and if it can find one, these new bushes will be full of juicy berries. So, at some point, the expected value of continuing at the current bush diminishes below the expected value of foraging other bushes, even given the risk that the animal won’t find another bush at all. At that point, it leaves to look for another bush.

Multitasking can be framed similarly. The task we are currently doing has some value, but that value may diminish over time while other tasks increasingly attract our attention. We work on Sudoku puzzles, but as we solve puzzle after puzzle our taste for the game starts to diminish. All the while, there are other attractive tasks with some value of their own. Maybe a peek at Instagram beckons. At some point, the value of the current task diminishes to the point that those other tasks are more valuable, and we switch.

Cognitive neuroscientist Roshan Cools has been among the pioneers in understanding cognitive control in motivational terms.[2] She has pointed out that framing multitasking as a foraging problem also reframes the stability-flexibility dilemma discussed in chapter 3. For example, holding a task goal in working memory is a good idea only if it is valuable to continue doing that task. In contrast, shifting to a new task will destabilize the current goal because it has either lost its value or other more valuable goals have supplanted it. Framed in this way, gating working memory is actually a decision about the value of our tasks. It follows, then, that working memory is a system not just for holding information active over the short term but for retaining information the system finds valuable or that will, by virtue of its influence on other processing in the brain, lead to future value.

Of course, the system cannot simply chase the most immediately appealing task value. For example, there are times when we have been working on a project for a while, and other tasks become increasingly attractive, but we nonetheless need to stay focused. Maybe we have to write a particularly touchy email to a coworker, finding the right words is a challenge, and we’d rather be doing just about anything else. In such circumstances, we can and often do shift, finding ourselves a half hour deep into the latest escapades of the Kardashians instead of doing what we need to get done.

However, this kind of value-chasing task switch is a failure of control, not a success. In these moments, our control system is needed to resist the impulse to switch, to keep us on task or to shift us back to the task we should be doing, should the Kardashians prevail. So, the control system must somehow leverage the more subtle and abstract value that leads us to stay on our immediately difficult or boring task so that we can achieve a higher value in the long term.

In this chapter, we will consider cognitive control from this motivational perspective. This is important because, like akinetic mutism, many failures of cognitive control can be traced back to a motivational cause rather than one of competency or mental efficiency. Likewise, conceptions of motivation factor into our most fundamental ideas about self-control, mental fatigue, and willpower. Thus, understanding control requires understanding its relationship to motivation.

Importantly, however, there are two sides to motivation. First, there is the value we gain by performing a task. This outcome-based value is what most of us associate with motivation; it’s the prize at the end. Cognitive control helps us get the prize by allowing us to better shape our behavior to match our situation, making success more likely, even when tasks are complex or novel. However, another side to motivation is often missed. We likely have to do some physical and mental work to get the prize. Engaging in mental work, just like physical work, comes with an aversive experience of mental effort, and that experience is particularly found in tasks requiring cognitive control. Mental effort is a cost that discounts the net value we gain from performing the task. Thus, our motivation to engage in control involves weighing both what we stand to gain and the work required to get there—in other words, there are costs and benefits of control. In the following sections, we will take a look at scientists’ efforts to understand both sides of this motivational coin and how the brain’s control system computes these trade-offs. We’ll start on the benefit side of the ledger and consider what psychology has taught us about how we assign value to our tasks.

Value and Control

Any theory of decision and choice requires a value function. Value functions describe the relationship between some state of affairs in the world and the degree to which that state is desirable. For example, Figure 7.1 plots the classic hypothetical value function between monetary gains and the value people place on that monetary gain according to Daniel Kahneman and Amos Tversky.[3] Behavioral economists refer to the value axis as subjective value, which acknowledges that the value in question is dependent on the person doing the valuing. For example, a college student might place a higher value on receiving $50 than Bill Gates would. My kids are over the moon when they find a penny on the street, whereas I can’t even be bothered to bend down and pick it up. So, subjective value is just that, subjective.

You will notice that the value function plotted in Figure 7.1 is not a straight line. Each monetary gain or loss is not valued equally. Rather, the function is curved such that gains in value are much steeper near the origin. This means a gain of $5 feels considerably more valuable to people than a gain of $1. But, getting $1005 feels about equally as awesome as getting $1001. Both are a difference of $4, but the latter gain does not feel as valuable as the former. This curve will differ from person to person, but everyone exhibits a similar dampening function.

You will also notice that the curve is steeper for losses. In other words, people dislike a $10 loss more than they enjoy a $10 gain. This asymmetry reflects a second well-established phenomenon called loss aversion: once people have some capital, they do not like to lose it, so they are generally less willing to take risks that incur losses than acquire gains of the same amount.

Value functions can conceivably come in many different forms, and the brain must have a means of computing them if we are to make motivated decisions about what task to perform and when. Abstract task goals, like writing an email or taking a walk, are somehow translated into an expression of value that the brain can use to make decisions. We use these value functions to form preferences over seemingly limitless, arbitrary choices about what to do. For example, would you rather bake a pizza or plant some flowers? You probably have never been asked that exact question before, but you also could probably answer it. That we can make side-by-side comparisons about such arbitrarily different tasks also suggests that, at some level, we can reduce these complex situations to a common-currency value function, on the fly, and without prior experience. Nonetheless, beyond evidence that the ventromedial prefrontal cortex is important for these abstract, inferred kinds of value-based decisions,[4] it remains mostly mysterious how the brain computes its value functions and what laws govern this process.

FIGURE 7.1. The asymmetric value function from Kahneman and Tversky that relates the outcome of any prospect, in terms of absolute gain, to the subjective value of that outcome.

This gap in our knowledge is no more evident than in the fact that we can’t yet program a general-purpose value function into an artificial intelligence. This is one reason why a true generalist AI is still a long way off. Most successful AIs are programmed with very specific value functions such as valuing doing the things that win a game of Go. That can work well for learning how to play Go, but the AI can’t really use that value function anywhere else.

Matt Botvinick, whom we met earlier in this book, is a leading cognitive neuroscientist and influential cognitive control theorist at Google Deep Mind. Matt gives the following example to illustrate why it is hard to give AIs an all-purpose value function.

Imagine we built a home robot with the broad goal of serving humans. What value function should we give this robot? Perhaps, it should value “making humans happy.” But how does it do this? How about by making us money? Money makes us happy. So, your robot sells your dog.

Regardless of how value is computed by the brain, however, once we do decide that an outcome or task is valuable to perform, cognitive control is affected by this valuation. Some of the best evidence for this relationship comes from experiments that incentivize control.

These experiments follow a straightforward recipe. You take a cognitive control task and offer a reward for performing quickly and/or with high accuracy. Then, you test how performance changes based on the level of incentive proffered. So, for example, during a Stroop task, prior to presenting each word, you might offer either a big reward, say $1, or a small reward, 5¢, for a fast and accurate response. The critical question is whether Stroop performance will change depending on the amount of incentive on offer.

Not surprisingly, performance on a task improves in general when a higher reward is at stake.[5] Importantly, however, the improvement in performance is specifically attributable to the contributions from cognitive control in the task. Consider the case of Stroop. As we saw with CDM, cognitive control specifically confers enhanced focus on the task-relevant feature of the input, the ink color of the word, rather than the word itself, in determining the response. Thus, when incentivized to perform well on the task, people engage control to direct focus more to the ink color than the word, reducing the interference when they mismatch. Of course, this increased focus on the ink color also reduces any benefit that might be gained when the ink color and the word match. This pattern is precisely what is observed in real experiments. Stroop interference is reduced when the ink color and word are incongruent, and people are incentivized, but there is also less benefit when they are congruent.[6] Thus, if people are motivated to rapidly improve their performance on a task, they do so through the cognitive control system. Put another way, using cognitive control has benefits.

The modulation of cognitive control by incentives is a widely observed phenomenon, evident across all major laboratory tests of control, from Stroop to stop-signal inhibition to task switching. But perhaps you are having trouble getting excited about these observations. We all know that when we really want something, we work harder to get it. Yet, these experiments actually provide some very important insights that might not have been obvious otherwise.

First, the fact that we can do better on a cognitive control task when properly motivated must also mean that we weren’t giving 110% in the first place (to quote my high school P.E. coach). In other words, cognitive control is not “all or none.” When someone does a Stroop task, in the absence of any explicit incentive or motivation, they do not give maximal engagement. Whatever is motivating them in the unincentivized case, be it the desire to do well on any task they set their mind to or to please the experimenter or whatever the case may be, this motivation was set at some level and led to a corresponding level of control allocation. Manipulating the incentive will modulate control above or below this level of motivation, accordingly.

Second, control is not merely a reflexive response of the system to the presence of conflict among multiple response pathways or the need to sustain a task goal. It is deployed because addressing that conflict or sustaining that task goal will yield a particularly valuable outcome. The more valuable the outcome is, the more that control will be allocated.

In the brain, offering incentives modulates dynamics in the same cortical and basal ganglia systems crucial for working memory gating.[7] One important way that value modulates control in this way is through the effects of the neurotransmitter dopamine, introduced in chapter 3. Neurons from deep subcortical nuclei in the midbrain, called the substantia nigra and ventral tegmental area, are major sources of dopamine to the striatum and the frontal cortex.

Neurophysiological studies have found that dopamine cells in the midbrain deliver important, informative signals to their cortical and striatal targets that help tie value to behavior. Seminal work by neurophysiologist Wolfram Schultz characterized the responses of the midbrain dopamine cells of the macaque money.[8] Schultz conducted a basic Pavlovian learning experiment wherein a tone always preceded the delivery of a reward, such as a squirt of juice, by a few seconds. Early in the experiment, the juice was met with a volley of activity from the midbrain dopamine cells. This makes sense if one associates firing of these dopamine cells with reward.

However, subsequent observations turned this simple view on its head. With experience, the animal learned that whenever that tone sounded, juice was coming a few seconds later. At this point, despite the fact that the thirsty animal still found the juice pleasurable, the dopamine cells no longer increased their firing to the juice squirt. Rather, they now fired to the tone that predicted the juice squirt. Moreover, if the tone sounded but no juice squirt arrived, the dopamine cells actually decreased their firing, suppressing below baseline at about the time the juice squirt was supposed to have arrived.

Computational neuroscientists Read Montague, Peter Dayan, and Terry Sejnowski have proposed that this pattern of data mirrors precisely what one would expect if midbrain dopamine cells are computing a critical learning signal called a reward prediction error.[9] This error is a central construct in theories of reinforcement learning from AI. It is computed simply as the difference between an expected and a received reward. Learning in these theories involves continuously and incrementally adjusting expectations of reward based on these errors of prediction.

For example, imagine you expect you will receive $5 every time a tone sounds. A tone sounds, and then you receive $2. Uh-oh, you received $3 less than you predicted, and so this is a negative prediction error ($2−$5 = −$3). Thus, you adjust your expectations regarding tones downward a bit. Now, when the tone sounds you expect only $3. This time you receive $4! Hey, this is also a prediction error, but it is a positive one, of $1 ($4−$3 = $1). So, you increase your expectations a bit and predict you will receive $3.50 the next time a tone sounds. Over time, this simple kind of learning system will eventually converge to the average expected value associated with the tone.

The responses of midbrain dopamine neurons appear to signal these prediction errors. Before the animal learns the relationship between the tone and the juice, the animal has no reason to expect that the tone predicts juice. In fact, it has no reason to expect the juice at all. So, when it suddenly receives a juice squirt, this unexpected reward is a positive prediction error that causes firing of the dopamine cells. After the animal learns that the tone does predict the juice, when that juice is delivered as expected, the prediction error is close to zero, and the dopamine cells do not change their firing. However, if the juice fails to arrive as expected, then there is a negative prediction error and, accordingly, a dip in firing.

A second interesting feature of these observations is the increase in firing to the tone itself. It is as though the reward-based firing moves backward in time from the juice to the tone over the course of learning. This is also a feature of reinforcement learning. In essence, the animal can learn for any context what reward can be predicted then and at all points in the future. Thus, the presentation of a tone is associated with value because it predicts future juice squirts. Since nothing predicts the tone, the tone signals an unexpected increase in expected value and so a positive prediction error at that point but at no point after that until after the reward is received. In sum, then, dopamine signals from the midbrain convey information about reward prediction error to its targets in the rest of the system.

One of the targets of these dopamine reward prediction errors is the striatum, and this input is one of the ways that corticostriatal gating can be connected to value. We alluded to this role for dopamine in chapter 3. Dopamine affects the firing of the Go and NoGo cells within the striatum differently. Go cells will fire more when dopamine levels are increased. NoGo cells are inhibited by dopamine and will fire more when dopamine levels dip. Thus, increased dopamine in the striatum will result in a Go bias. In other words, less evidence is needed to open the striatal-cortical gate and amplify an action or task context under consideration in working memory. By contrast, a dip in dopamine will result in a NoGo bias, which will decrease the likelihood of executing an action or activating a gate.

This relationship between dopamine and action execution is classically captured in Parkinson’s disease.[10] Owing to the degeneration of cells in the substantia nigra, the supply of dopamine to the striatum is greatly reduced in the disease. One consequence of reduced dopamine is an overwhelming NoGo bias in these patients. They are often unable to generate sufficient thalamocortical drive to move, resulting in the freezing and rigidity that are the hallmarks of this disorder. Those with severe progression of this disorder can end up trapped in their own bodies—able to think, understand, and contemplate actions without being able to execute them. One long-standing therapy is to replace dopamine by administering a precursor called L-dopa that the body can synthesize into dopamine. Though this approach has its limits, replacing dopamine can help offset the NoGo bias and aid symptom relief.

Putting this all together, dopamine modulation of gating is a clear way of connecting value to the gating actions of the basal ganglia. If a context comes along that predicts a value better than expected given the status quo, then there will be a burst of dopamine. This will make the Go cells more likely to fire and accordingly will encourage gating of the action or task representation under consideration. Conversely, a context or goal that comes along and predicts a negative outcome will be met with a dopamine dip. This will make NoGo cells more likely to fire, making it less likely that this action or task representation will be gated by the striatum. In this way, value directly modulates the gating actions central to cognitive control.

Dopaminergic signals to the striatum serve a second essential purpose, as well: they can support learning. Dopamine prediction errors result in long-term changes in the synapses of Go and NoGo cells such that they increase and decrease their likelihood of future firing to a set of inputs.[11] When outcomes of an action are better than expected, there is a positive prediction error and an increase in dopamine; Go neurons will strengthen their synapses. Conversely, NoGo neurons will increase their synaptic strength when outcomes are worse than expected and dopamine dips. This allows a long-run history of outcomes to be reflected in the decisions of Go and NoGo cells.

These learned actions can include not only motor responses to simple inputs but also higher-order working memory gating actions at the heart of control function. As we discussed in chapters 3 and 4, many control problems boil down to learning the right working memory gating structure. Reward prediction errors encoded by dopamine can train the striatum to gate the right context at the right time. Each corticostriatal loop in the gating system can learn on its own about what to gate in and out of working memory, at its level of the hierarchy, based on success with the task.

For example, when the system output gates the task demand “color naming” on color-naming trials, it achieves a positive outcome. This strengthens those Go neurons, making it more likely that the system will output color naming again under similar circumstances in the future. In sum, the impact of dopamine reward prediction error signals on the striatum is to increase the Go bias of the working memory gating, and so dopamine in the striatum enhances flexibility in line with expected value.

Dopamine signals from the midbrain also project to the prefrontal cortex, but here they have the opposite effect from that in the striatum; in the cortex, dopamine enhances stability.[12] Increased dopamine in the cortex enhances the ability of prefrontal cortex to maintain information over time and strengthens its neural representations.[13] Reduced dopamine has the opposite effect in the cortex, destabilizing these neural representations and making them more susceptible to interference and distraction.[14]

In other words, if we are holding a goal in working memory, but dopamine in the cortex is low, that goal is more likely to be knocked out of memory by another salient task. So, in the cortex, dopamine establishes the value of holding something in working memory. If we are engaged in a task that is high value, dopamine will make it more likely that the representation of that task will be stabilized in working memory and will continue to exert a prevailing influence over what we are doing. If the task is low value and dopamine is low, it is easier to lose that task demand from working memory.

Thus, dopamine can balance stability versus flexibility between the striatum and cortex.[15] Enhanced dopamine to the cortex enhances stability, allowing us to sustain tasks and goals that are of value to us. Conversely, dopamine’s influence on the striatum enhances flexibility and facilitates shifting to a new task or goal that holds higher value than what we are doing now. In this way, dopamine makes value an intrinsic piece of the corticostriatal gating mechanisms of cognitive control. Modulating gating based on value allows us to pursue courses of action that lead to positive outcomes and avoid those that don’t.

However, as introduced at the outset of the chapter, pursuing high-value benefits is only one side of the story of motivation. There are also costs to engaging in the mental work needed to attain those benefits that need to be taken into account when we decide to engage control and perform a task. How the brain computes these costs and uses them in our control decisions is a long-standing problem and a wide-open area of current research.

The Mental Labor Market

We make decisions about what tasks to do based on not only the value we stand to gain but the costs in mental work required to obtain that value. If I told you that you could earn $5 by adding three two-digit numbers in your head, you might take me up on that offer: if there is no cost to mental labor, then $5 offered is $5 gained. However, if I offered you the same $5 to complete every math problem in an elementary school math textbook, you might pass up the money. In effect, you performed a cost-benefit analysis, weighing the benefit of the task of doing mental math against the mental effort required. If you weren’t drawn to accept my second offer, then the cost of the mental labor must have exceeded the $5 you stood to gain.

One of the clearest places where this trade off unfolds is the Wild West labor-supply dynamics of Amazon Mechanical Turk, or “MTurk” to insiders. MTurk is a crowdsourcing marketplace run by Amazon, where requesters can post small jobs, termed “Human Intelligence Tasks” or HITs, and a mass of online free-agent workers called “Turkers” can sign up to complete them for a small monetary compensation.

The tasks vary widely in their demands, including categorizing different kinds of foods, labeling the content of pictures, doing web searches, or writing a 450-word essay. Usually, the tasks are simple, albeit tedious, for humans to do, but they are hard to impossible for a computer. So, a large group of humans each doing a little bit of the work can complete a massive task of this kind very quickly.

Typically, HITs are relatively short to perform, and the compensation is meagre. That 450-word essay, for example, might be worth a quarter to complete. And, that’s probably on the high side in the grand scope of HITs. A simple task like categorizing an image as containing people or not might earn pennies. A recent study of more than 2500 hundred Turkers performing almost 4 million HITs found that the median hourly wage earned by workers on MTurk ranged from $1.77 to $3.18, depending on the method used to compute the wage, and there was a great deal of variance in these estimates.[16] The upshot, however, is that this kind of unregulated mental labor market does not pay well by anyone’s standards, but people do the work.

The ethics of such an unregulated workplace notwithstanding, the MTurk marketplace provides a natural environment to observe the relationship between mental labor and value. Turkers freely choose what tasks they want to perform. Requestors have to attract Turkers to their jobs. Amazon encourages everyone to consider the expenditure of time relative to the money earned. Notably, however, it does not mention anything about the mental effort involved. Nevertheless, Turkers’ decisions to take on a HIT do take account of mental work being requested.

Computer scientist Panos Ipeirotis writes a blog about computational and data-science issues in the business world. He wrote a series of posts based on an informal study in which he asked Turkers why they do and do not complete HITs.[17] The quotes from their responses that he posted on his blog are quite intriguing in light of our current discussion. The following are some examples, with my own emphasis added:

“The tasks I don’t like are tasks that have many, complicated steps.”

“In order for me to undertake a task, it has to be either interesting, or very easy to do.”

“What attracts me to participate in a particular task is if it is relatively simple, quick, and easy to complete.”

“I only select simple tasks that I can finish while doing something else, e.g., watching TV/surfing.”

Time and money, as Amazon advises, certainly show up as regular considerations in Turkers’ responses, but the difficulty of the task or how much attention it demands is also common. In broad strokes, Turkers tend to dislike tasks that put heavy demands on cognition generally and, perhaps, cognitive control in particular.

The influences of mental labor are reflected in the compensation offered for different kinds of tasks. That same large-scale study of earned wages also investigated the relationship between different types of work and the wages they paid.[18] The study found that simple mental tasks, like finding company contact information on a webpage or basic audio transcription, yielded a lower wage on average than did more open-ended tasks, like logo design or describing the contents of a video.

MTurk is a free marketplace, so requesters will generally offer the lowest compensation needed to get Turkers to accept a HIT. Thus, these wage differences across categories of work align well with the testimonials from the Turkers themselves. They prefer simpler tasks, and, consequently, they are willing to take lower compensation to complete these tasks.

Thus, whereas we can be motivated to perform a task because it yields valuable outcomes, that value is discounted by the mental labor involved. Mental work is a cost that reduces the net value of an outcome. If you want someone to do more thinking for you, then you need to compensate them accordingly (cc: deans everywhere). Indeed, there has been at least one experimental demonstration, by Wouter Kool and Matt Botvinick, suggesting that the decision to engage in cognitive work follows the fundamental principles of labor-leisure trade-offs from economics.[19] So, understanding the decision to engage in a task has to account for not only the incentive but also the cost of mental labor.

The fact that mental labor can be aversive and experienced as a cost is a long-standing idea in psychology, and it dates at least to the earliest studies of mental fatigue. For example, in the late nineteenth century, applied psychologists were asking questions like, how much mental work can students sustain in school? A predominating early view was that aversive feelings of mental fatigue are a direct consequence of a loss of mental efficacy, and further, that our efficacy, once diminished, affects everything, whether mental or physical. Indeed, at least one psychologist (Bettmann) in 1892 declared that engaging in exercise, like gymnastics or walking, was not a suitable way for students to relax after doing heavy mental labor.[20] After all, if mental labor drew on the same energy pool as physical labor, how cruel to make students exercise after a long day of thinking!

Renowned learning psychologist Edward Thorndike was skeptical of these ideas. In his own experiments, he had found that though people certainly disliked being made to do long mental labor, they could still perform various tasks effectively, even after long bouts of work.[21] For Thorndike, there was a difference between the experience of effort as a motivational matter, and the actual efficacy to do mental work. The former was weighed in the decision to work, whereas the latter affected the work itself.

Thorndike’s doctoral student at Columbia, Tsuruko Arai, conducted a landmark study of mental fatigue that she published in 1912.[22] Arai was the primary subject of her own experiments. She set herself the task of continuously multiplying randomly generated four-digit numbers with each other in her head, like 2645 times 5783. She practiced this task daily for a month until she could memorize the numbers at a glance and perform the mental multiplication with her eyes closed.

Once she was satisfied that she would not improve further with practice, Arai began her fatigue test. Over four consecutive days, she continuously solved four-digit multiplication problems from 11a.m. to 11p.m., pausing only 2 to 3 seconds between problems in that 12-hour period. For each problem, Arai recorded her answer and the time to complete it. She also recorded her subjective experience during this process. I’m no pushover for mental labor, but even the thought of this much continuous mental work makes me cringe.

Arai completed her four-day marathon successfully, and over the course of her 12-hour days of mental work, her performance did suffer substantially. Arai exhibited a fourfold slowing in her problem completion rate. She described how difficult it was for her to keep the numbers in her head and avoid intrusive thoughts.

Further, the experience of doing math problems for 12 hours a day was associated with an aversive subjective experience of mental fatigue. In her own words, she experienced “emotions of repugnance at the thought of certain types of mental activity, amounting sometimes to a sort of mental nausea.”

Importantly, however, she found only a small relationship between the changes in her performance and her ratings of her subjective experience of mental fatigue over the course of the day. In referencing this work, Thorndike later strongly argued against a depleted mental resource. He claimed that, though fatigued, Arai never reached “empty”: she could always have done another math problem. Her motivational state was distinct from her mental efficacy.

There are some obvious problems with an experiment conducted on a single person, particularly if that person is also the experimenter and a pretty remarkable individual. And, by any standard, Tsuruko Arai was a remarkable individual. In 1912 she became the first Japanese woman to earn a PhD in any field. She returned to Japan, where she taught and ran a lab at a university, published an extended version of her graduate thesis as a monograph in Japanese, published a Japanese translation of Francis Galton, penned a memoir called Happy Memories to advocate for women’s education based on her experiences in the US, and had two children along the way. And all this she accomplished in only three years. Tragically, Tsuruko (Arai) Haraguchi passed away from tuberculosis in September 1915, at the age of 29. She was an amazing and inspiring woman, so much so, in fact, that one might reasonably be concerned that her data are not representative of the rest of us when it comes to mental labor!

Nevertheless, Arai’s seminal studies on mental fatigue were replicated in later years by other scientists. Notably, a group of three graduate students at the University of Southern California in 1946 reproduced Arai’s methods, right down to the marathon math sessions.[23] It was again a deeply unpleasant experience for all. One student remarked that she would not do this again for $10,000 (that’s about $130,000 in today’s dollars).

All three students found that, if anything, they did better than Arai had done, even improving their performance by some measures over the course of the fatigue session. They attributed this improvement to being competitive with each other and with the standard for performance that Arai herself set. Thus, there can be little doubt that our performance will decline over time, but in line with Thorndike’s supposition, with more motivation—in terms of the expected value of performing well—people can nevertheless improve their performance, even when they are experiencing enhanced mental fatigue. There is always something left in the tank, even after lots of mental work.

This disconnect between the subjective experience of effort and ongoing mental efficacy is consequential because ideas about resources and mental effort, particularly concerning cognitive control, remain influential to this day and have seeped into the broader intellectual consciousness. For example, in an interview with Michael Lewis for Vanity Fair, Barack Obama, who is a pretty good barometer of what an informed, well-read intellectual currently thinks about the world, said “I’m trying to pare down decisions. I don’t want to make decisions about what I’m eating or wearing. Because I have too many other decisions to make.” Lewis noted that Obama “mentioned research that shows the simple act of making decisions degrades one’s ability to make further decisions.”[24]

Perhaps the idea that the mind is a muscle that can be fatigued is intuitive for us because we can draw an analogy to physical effort. For example, one hypothesis about feelings of fatigue during physical exertion is that they provide a warning signal that we need to stop whatever we are doing, whether lifting a weight or jogging a 5k, because our muscles may soon give out entirely. In this case, fatigue is connected to activity-dependent metabolic changes, whether through the depletion of substrates or buildup of metabolites, that will ultimately prevent the muscle from contracting, no matter how much we want to contract it. So, it makes sense that a feeling of fatigue provides a sort of stop signal in advance of that catastrophic failure.

Yet, this seems a poor analogy for the aversive experience of mental effort. It is likely not the case that the feeling of mental fatigue we experience after a long and hard bout of mental work or studying is a warning that we are nearing a mental collapse. Indeed, it is not clear that it is ever possible to bring about a mind stoppage on par with muscle fatigue just by doing lots of mental labor. Assuming your basic bodily needs of food, sleep, and water are taken care of, you could never do so many math problems, for example, that you couldn’t possibly do another, even if you wanted to. Rather, a reasonable heuristic would be if the motivation is there, so is the mental capacity. But the motivation is not something to take for granted.

Nevertheless, the conception of mental effort as depleting a finite mental resource remains a highly influential model, particularly in popular conceptions of mental work. In fact, it is a reasonable guess that these ideas, whether directly or indirectly, influenced the earlier Obama quote. So, given their influence, it is worth evaluating this viewpoint more thoroughly.

The primary claim of the resource model is that when we engage in a difficult act of self-control, like resisting eating a cookie, we expend our general mental resources. Because these are general resources, their depletion means that not only will we have a tough time resisting the temptation of another cookie, we will also have a bit more trouble with other tough decisions that have nothing to do with cookies, like deciding what shirt to wear or what show to watch on television. In other words, we have expended a general mental resource, and so its depletion in one setting will transfer to entirely different settings that all depend on that same pool of resources.

The primary scientific evidence for this hypothesis started with a seminal series of experiments by Baumeister and colleagues in 1998.[25] These studies utilized a procedure they term ego depletion. First, participants engaged in some activity requiring cognitive control, such as repeatedly forgoing a chocolate in order to eat a radish. After this task, people were asked to perform a second demanding task, also requiring control, like an anagram puzzle. These early studies found that people would give up more quickly or performed with low accuracy on the second task relative to controls. The researchers explained that participants performed more poorly on the second task because they had sapped their willpower doing that first task, leaving them with less of this general resource to use for the second, challenging task.

Evolutionary psychologist Robert Kurzban highlighted several problems with this perspective.[26] (His commentary[27]pointed me to the Obama interview cited earlier.) One of his major criticisms effectively echoes those discussed previously. If a resource is being depleted, there is no evidence it ever reaches empty, even after hours of work. On the contrary, as we have noted, incentives and motivation can increase mental effort and induce performance improvements. Indeed, this is even the case when someone has engaged in considerable prior mental work. That this is possible is a major problem for a resources account. If resources are directly tied to the efficacy of one’s self-control, how do we muster more merely because we are incentivized? If I have lifted a weight to the point of muscle fatigue, I can’t lift any more weight, no matter how much incentive I am offered to do so.

The response to this critique from proponents of the resource view has been that people must have control over their resource expenditure.[28] They can budget how much mental resource they deploy based on motivation, and this makes resources available for them to use should better incentives come along.

To illustrate, consider the example of Halloween at our house. For reasons that are not fully clear to me, our neighborhood is inordinately popular with kids from all over Providence, Rhode Island. Maybe it is the closer density of homes in the area, but whatever the reason, they show up by the vanload. We always think we’ve bought enough candy, but every year, it becomes clear we have underestimated matters yet again. As a result, as the night goes on, we slowly downgrade from a generous handful of candy to each child, to three pieces, to two, to one, in an effort to make our dwindling candy reserves last a bit longer. So, perhaps people do the same with their fixed capacity of mental resources. As their capacity gets depleted they get stingier and stingier about using it, giving out only one piece of mental candy per tough decision rather than a handful. But if they do have incentive to perform a task following depletion, they have resources available to allocate more and improve performance.

This is certainly a plausible way to reconcile the data with the theory, but it is still somewhat dissatisfying. First, even pacing ourselves, there should be some point at which all our resources are depleted. We do run out of candy eventually at Halloween. Twelve hours a day for four days straight spent doing hard mental math without a stoppage would suggest that Arai either had a pretty big reservoir or was remarkably good at budgeting it, so much so in fact that it is unclear why we would be so stingy about deploying our resources over the short timescale of an ego depletion experiment. Certainly, one need not worry about routine momentary decisions like deciding what to eat affecting more important decisions later on.

Perhaps an even worse issue with this resources-plus-motivation account is that this view can predict anything. When people do worse, they have depleted a resource. When they do better, they were motivated. Whatever your experiment, whether you find effort caused depletion or not, you can explain it after the fact with just the right mix of motivation and resource depletion. That’s handy if you never want to be wrong. But, it is also not a theoretical framework that is testable using the scientific method. So, without more constraints, the resources-plus-motivation account cannot be falsified and so is not a scientific theory.

A further problem for the general-resources perspective is empirical, namely, the establishing experimental evidence for ego depletion has recently been called into question. Until recently, the effect of ego depletion appeared to be among the most robust observations in psychology. For example, one meta-analysis conducted in 2010 based on 83 published studies found depletion effects to be a reliable effect of moderate size.[29]

However, follow-up work conducted only a couple years later made it clear that this conclusion may have been premature, and matters are substantially muddier. A careful look at the studies used by the previous meta-analysis suggested that the moderate effects found in that study may have been subject to misleading statistical biases.[30] An updated meta-analysis of ego depletion correcting for these biases found little evidence of an effect.[31]

In light of these controversies, 23 laboratories set out to test the phenomenon of ego depletion in a multilab replication effort led by psychologists Martin Haggar and Nikos Chatzisarantis, the same scientists who had conducted the original meta-analysis.[32] The labs registered their hypotheses and methods in advance of collecting the data. The task procedures were reviewed by independent scientists, including resource theorists, like Baumeister himself, to ensure the methods were reasonably likely to find an effect if it is was there. Shockingly, though all but one of the labs had hypothesized that the depletion effect would be observed, only 3 of the 23 labs found an effect that differed from zero, and for one of these, the effect went in the opposite direction; depletion seemed to improve performance on the next task! Thus, ego depletion failed to replicate using these procedures.

It is important to note that this failure to replicate ego depletion used only one experimental setup to test the depletion procedure. Thus, it is conceivable that the problem was with this specific setup and not ego depletion overall. In a published response to the failed replication, Baumeister and colleagues made precisely this argument.[33] And though they had signed off on this procedure before the trial, they qualified that this was only a reserved “go ahead and try” sort of preapproval rather than a “this is sure to work” kind.

You can come to your own conclusions about how convincing this line of argument is, but it is worth noting that matters have not improved since that 2016 replication failure. A 2018 study of the Many Labs project that tested 2000 college students on various measures of their cognition looked at the impact of doing a Stroop task on how long a student would last trying to solve an unsolvable anagram problem right afterward.[34] This again seemed like the prototypical depletion case. Indeed, unsolvable anagrams was one of the tests used in the original 1998 study of ego depletion. In the Many Labs study, however, there was no evidence of depletion on anagrams, despite a massive sample size that would enhance the ability to find even a tiny effect. So, though the matter is far from settled, there are receding empirical grounds for proposing that we have a general, depletable mental resource that is taxed by self-control.

A final issue with the resources account of mental effort concerns what this resource actually corresponds to in physical terms. What in the brain is being depleted or budgeted by these difficult decisions?

Baumeister and colleagues have suggested that glucose might be this resource.[35] Glucose provides energy to every cell in the body, including the neurons in the brain. Thus, the brain has to metabolize glucose continuously and will metabolize more where neurons are more active. Baumeister has proposed that it is glucose metabolism that is tapped by heavy mental work and so this is the resource the brain is also conserving by signaling mental effort.

Again, the basic empirical support for this view has been called into serious question. Another meta-analysis conducted in 2016 found little consistent experimental evidence that glucose is reduced following hard mental work, that glucose levels limit subsequent mental work, or that glucose can be restored to reverse the impact of ego depletion.[36] Thus, as with the ego depletion effects in general, the experimental evidence supporting this hypothesis is not holding up well.

The ambiguity of the evidence for the glucose hypothesis notwithstanding, there is further reason to be skeptical about the basic assumptions of this view. As has been pointed out by several critics of this position, merely opening one’s eyes results in a large increase in glucose metabolism in the brain, as the brain’s perceptual pathways perform many computationally demanding processes on the input. Yet, we do not experience mental effort when we merely open our eyes and process a scene. Thus, increased metabolism of glucose alone cannot be the correlate of the experience of mental effort.

It should be noted that there may be other correlates of resources that might work better than glucose. For example, computational cognitive neuroscientist Clay Holroyd has proposed that a substance called amyloid might be at the base of a resource theory of mental effort.[37] Buildup of amyloid in the brain is a well-known correlate of dementias, such as Alzheimer’s disease. Holroyd suggests that the presence of amyloid may affect the efficiency of neural processing, analogous to the way that certain metabolites can cause muscle fatigue during physical effort. To avoid this buildup, Holroyd argues that we must regulate our demands on heavy neural processing over the long term, and so the brain places a cost on this kind of processing, in the form of mental effort. Views such as this are intriguing and if verified with data, might ultimately salvage some form of resource model. However, until then, we had best look elsewhere for gaining some understanding of mental effort costs.

If mental effort costs do not reflect the depletion of a fixed capacity of mental resources, what does make a task feel effortful? What about a task drives the brain’s cost-benefit analysis? This is a crucial point to clarify because without it, many theories that rely on a concept of mental effort suffer from what logicians call circularity.

Circular reasoning is a logical fallacy in which the premise of an argument is used to generate a conclusion that is then used to support the initial premise. For example, if our argument is that we avoid tasks because we find them effortful, how do we know that a task is effortful? If the answer is, because we avoided it, we’re in trouble. Why did we avoid it? Because it was effortful. Why is it effortful? Because we avoided it. And so on until the sun dies. This obviously can’t work as an explanation.

To avoid this circularity, we need a way of identifying what about a task makes it effortful independently from our tendency to discount its outcomes. In other words, we need to identify a definable quality of a task that would constitute effort and so cause a greater tendency to discount a task outcome.

One influential hypothesis along these lines, initially proposed by Kurzban, is that effort costs deter us from performing tasks that tie up our mental apparatus and so prevent us from doing other tasks. From this point of view, the experience of mental effort has evolved in us to signal the problem of simultaneity, or the limit on our ability to do multiple rewarding things at once.

Because of our limited ability to multitask, which we discussed in chapter 5, when we focus on one task, then we are effectively missing whatever gains we would obtain from other tasks we could be doing. Economists label this an opportunity cost or the price we pay for taking one option because it prohibits us from taking another one. While we are busy doing a difficult task, like writing that sensitive email or doing our taxes, we are not watching Game of Thrones or catching up with friends on Facebook. So, according to this theory, we experience this opportunity cost as the aversive experience of mental effort while we do our taxes.

This hypothesis might lead to the prediction that we should feel mental effort and fatigue by having many things we need to do, regardless of the specific task we are doing. In other words, it feels worse to be doing taxes when we also still need to mow the lawn and do the dishes than when taxes are the only thing left to do. In examples like this, it is hard to separate the opportunity cost of having lots of tasks not completed versus the additional time demanded by completing these tasks later. But this caveat notwithstanding, there is some evidence that having lots of goals stacked in your head is an aversive experience to the degree that they come to mind.

As one example, we often experience spontaneous thoughts that are independent of what we are doing.[38] We usually associate these with daydreaming or mind wandering or with the moments just before sleep, but they can actually occur anytime during our day, busy or not, and are commonly unconnected to what we are presently doing.

Scientists can study the content of these spontaneous thoughts by sampling people randomly throughout their day and asking them if they are currently thinking about the task at hand or thinking about something else that spontaneously came to mind. Studies of this kind show that our spontaneous thoughts are mostly about ourselves and tend to have a “future bias.” In other words, they are more commonly concerned with future events that we might take part in, rather than events of the present or past. Nevertheless, whether of the past, present, or future, spontaneous thoughts are associated with activity in the same ventromedial frontal and medial temporal lobe systems important for episodic future thought.[39]

With relevance to mental effort, spontaneous thoughts are also commonly about unfinished tasks and goals. People report that having these thoughts about unmet goals is usually a negative experience. So, in a sense, we carry around a latent “to-do list” in our head, and items from this list occasionally jump unbidden to mind, akin to a notification on our smartphone. When they do come to mind, these thoughts cause us a bit of anxiety.

It is tempting to speculate that these negative, anxious feelings are actually adaptive for us. Doing the tasks on your mental to-do list helps relieve that anxiety, and so that relief is a natural driver to complete the goal stack in our heads. Indeed, one could further speculate that a consistent inability to complete those goals, to stop thinking about them, or to remove them from our “goal list” would result in persistent negative emotions. At an extreme, this could contribute to mental health problems. Perhaps consistent with this hypothesis, there is some evidence that the frequency of spontaneous thoughts is associated with a greater risk for mood disorders like anxiety and depression.[40] Thus, it seems likely that having lots of tasks to do does take a toll emotionally on us and could contribute to aversive experiences of mental effort.

However, it is unlikely that maintaining a deep goal stack is the entire answer to the experience of mental effort we feel when we perform a difficult task. For one, spontaneous thoughts, though common, are not constant. Nevertheless, we feel the effort of any difficult task whether we spontaneously think of other tasks to do or not. Conversely, if costs were computed through a more sustained or active process, doing this constant counterfactual thinking about what we “could be doing” would be itself quite demanding. And we certainly find tasks to be hard regardless of whether we have other tasks to do or not, so it seems unlikely that feelings of mental effort are simply computed based on the other tasks we could be doing.

Importantly, however, the idea of opportunity cost does not require that we literally compute all the hypothetical outcomes we could have had if we had done every other task around us. Rather, we can estimate the opportunity cost based on the task we are doing right now.

Assume that at any given moment there are innumerable tasks of different value we could be doing. Sometimes they are of less value and sometimes they are of greater value, but over the long run, there is an average value from other mental tasks available to us. This means that in the long run any task we are doing that ties up our system will tend to lose that value on average. So, it is reasonable to estimate the lost value based on the task we’re doing, rather than the tasks that we’re not. We can tax tasks with bottlenecks in our processing system because these prevent simultaneity. They will lose more value in the long run than tasks without such bottlenecks. That Turker who defined “simple tasks” as ones that she or he “can finish while doing something else” might have been on to something!

As we discussed in chapter 5, mechanisms needed for cognitive control are often bottlenecks in tasks because acts of control routinely rely on general, abstract task representations shared across tasks. Thus, a reasonable heuristic is that cognitive control will be taxed as costly if it is needed by a task. If this is so, what is tracking that cost in the brain and how does it affect the control system? Interestingly, much of the empirical and theoretical ideas in this domain focus on the same part of the brain discussed in the beginning of this chapter in association with the case of Elsie Nicks and akinetic mutism, the anterior cingulate cortex. The anterior cingulate affiliates with the cingulo-opercular network we met in chapter 3 and that is distinct from the frontoparietal network. Its mechanisms and relationship to cognitive control have been the focus of much research and debate. In the next section, we will take a closer look at these mechanisms for assessing the costs and benefits of control.

Anterior Cingulate Cortex and Monitoring

Akinetic mutism is on the extreme end of a spectrum of motivational disorders that stem from damage to the anterior cingulate cortex. These disorders express in terms of unusual responses to situations and circumstances that would typically invigorate healthy people. This association has prima facie led scientists to view the anterior cingulate cortex as an important node in the network for motivated behavior. As a result, its relationship to cognitive control mechanisms has been a topic of considerable focus and debate. The progress of these ideas is important for understanding how the brain might monitor the need for control in tasks and, indeed, its costs. So, let’s take a closer look.

Our story begins with the CDM model that we met in chapter 3. Recall that CDM was an important innovation because it demonstrated how information held in working memory can provide top-down contextual signals to control behavior. And further, it did so without recourse to a homunculus that set things up “just so.” Rather, the network used experience with the task to set up the right connections between input, output, hidden, and task demand layers.

Important as this innovation was, however, CDM had no way of knowing how well it was controlling itself while a trial was happening. Maybe it was providing enough top-down support or maybe not; it had no way of knowing. Its learning mechanism relied on the outcome of a trial to adjust its behavior on future trials. This would be akin to receiving a distracting text while driving but not adjusting your focus on the road until after you noticed yourself checking the text. We don’t do this. We resist these unexpected distractions on the fly, adjusting our control to match the task demands that come our way.

Online adjustments are a central feature of task control that earlier models had sought to achieve. For example, recall that the TOTE model discussed in chapter 1 included two “Test” steps between the Operate and Exit stages. These allowed the system to control itself on the fly. So, while CDM went beyond its predecessors by adding learning, it lost the control.

Matt Botvinick and Jon Cohen put control back into the CDM model by adding a new node responsible for monitoring conflict.[41] In a Stroop task, problems arise when the stimulus maps to two different responses at once, and the responses compete with each other. In these circumstances, contextual control is needed to adjudicate the competition. Further, the more competition there is, the more control that is needed to overcome it. In CDM, more control means a stronger, sustained signal from the task demand layer.

Thus, the new monitoring node simply received input from each response unit in the response layer of the CDM model and computed a conflict value based on their degree of coactivation. In other words, the conflict value would be higher to the degree that both responses were active together and at roughly the same level.

This matches our intuition about competition. Consider two tennis players. The most competitive matches are between two players of comparable ability, whether they are both pros or both amateurs; their comparability makes the game competitive. Thus, if both responses are active, there will be more conflict, which will activate the CDM monitoring units more. If one response came to be more active and the other diminished, the conflict value would decline.

The conflict-monitoring node continuously projected its output to CDM’s task demand units. This allowed the current level of conflict read out of the response layer to amplify or diminish the intensity of the task demand output. Thus, the more competition between the responses, the greater the top-down contextual influence to resolve it. Thus, by adding the conflict-monitoring node, CDM had a feedback control loop.

Botvinick and Cohen hypothesized that the anterior cingulate cortex specifically served as this conflict-monitoring node in the brain, receiving inputs from candidate response pathways and invigorating frontoparietal control systems accordingly to reduce conflict and make a response.

This hypothesis became the dominant view of anterior cingulate function for a decade or more, inspiring many studies. And initial returns were good. For instance, across several neuroimaging experiments using different cognitive control manipulations, greater activity was repeatedly observed in the anterior cingulate cortex when response conflict was high, such as in incongruent versus congruent Stroop decisions.[42] This activity pattern was also observed to be distinct from context-maintenance activity in the lateral prefrontal cortex, consistent with the separation in CDM between task demand and monitoring layers.

As experimental work continued, however, the conflict theory began to strain under new observations. Physiological recordings in animals repeatedly failed to find evidence of cells in the anterior cingulate cortex that were tuned to conflict. Rather, more commonly, these studies found cells tuned to value predictions, outcomes, errors, and the various responses the animal was making.[43] Meanwhile, studies of patients with focal damage to the anterior cingulate cortex failed to exhibit clear deficits in the adjustment of cognitive control based on changes in response conflict.[44]

In this context, computational cognitive neuroscientists Josh Brown and William Alexander proposed another model of anterior cingulate function that they termed the Predictions of Response and Outcomes, or PRO.[45] Like CDM, PRO is a neural network model that learns from experience. The anterior cingulate cortex in PRO has access to stimuli input through the senses and action plans that arise given those inputs, and from these, it can predict what responses will be made and what outcomes will follow from those responses. This would be akin to seeing a cake, knowing there is a plan to bite into the cake, and thereby predicting both the chomp your mouth will make and the tasty experience that will follow.

In essence, the model learns to predict response-outcome associations. I should clarify that this is subtly different from the associations of value discussed earlier in the chapter, like the juice squirt being predicted by the tone and influencing midbrain dopamine neurons. Those are associations between a stimulus and an outcome. Here, the association of a response and an outcome, biting and tasty cake, is being predicted based on the context and action plan.

Importantly, cells in the model’s simulated anterior cingulate cortex will fire for these predictions of response and outcome, and then are suppressed if those predictions come to pass. This means that the more options there are for a task, the more predicted responses and outcomes there will be, and so the more activity there will be in the cingulate. This would account for greater activity in incongruent conditions of a Stroop task, for instance, where both the ink color and word predict different responses.

Further, if the response or outcome prediction is wrong, this activity will not be suppressed. This is another kind of prediction error signal. However, unlike a dopamine reward prediction error, the prediction errors driving anterior cingulate cells in PRO occur when an expected response or outcome does not occur, regardless of whether the outcome is positive or negative. In other words, they will fire just as much for a pleasant surprise as for a rude awakening; this signal just means that a predicted response and outcome failed to happen.

The PRO model has fit a number of previous findings from anterior cingulate that had been explained in terms of response conflict, in addition to a wide range of physiology and neuroimaging data about the anterior cingulate cortex that the basic conflict-monitoring hypothesis could not explain. However, while the PRO model demonstrates how outcome prediction and error could explain physiological responses in anterior cingulate cortex, PRO leaves open what the brain does with these prediction signals.

One possibility is that the prediction signals can anticipate upcoming errors and lead to rapid error correction. As one example, Mandana Modirrousta and Lesley Fellows studied five patients with lesions to anterior cingulate cortex performing a conflict task called the Eriksen flanker task.[46] In this task, participants are shown arrows in a line and press a left or right button depending on the direction of a central arrow while ignoring the direction of some flanking arrows. The flankers can face the same direction as the central arrow, as in “>>> >>,” or they can face a different direction, as in “<<><<.” In the latter case, healthy people respond more slowly and are more likely to slip and press the wrong direction entirely. These difficulties arise because of response conflict between the left and right responses, which are both cued when the arrow directions are incongruent. In this case, more attention must be paid to the central arrow to overcome this conflict.

Often, when people make an error on this task, they slow down on the next trial. This is sometimes taken as a post-error adjustment in control, slowing oneself down to prevent further error. The cingulate patients tested by Modirrousta and Fellows also showed normal post-error slowing, despite their lesions. Likewise, they reported being consciously aware of their errors at the same rate as controls.

However, the patients differed from healthy controls in one important respect: their speed of error correction. In one experiment, participants were allowed to correct an error by making a second button press quickly after they pressed the wrong key. Healthy people will make these corrections almost immediately, around 200 milliseconds after they make the errant response. This response time is so fast it is about half that taken to respond normally in the flanker task, around 400 milliseconds. In other words, their correction speeds are so fast they are likely initiated before the error is actually committed; they are based on a prediction of an upcoming error.

This kind of rapid error correction was absent in the cingulate patients. They corrected their errors, but it took them around 700 ms, even slower than the time they took to make the initial response. This means they had to witness themselves commit the error to recognize it and plan a correction. Owing to their brain damage, they were unable to rapidly translate predicted errors into the drive necessary to proactively take actions to correct them.

A further clue to the role of anterior cingulate cortex in monitoring our control comes from the experiments on the experience of mental effort and motivation in control. Consider the case of patient RMB, who had a large lesion of the prefrontal cortex, including the anterior cingulate cortex, due to stroke.[47] Despite her large lesion, RMB behaved normally in response to conflict during performance of a Stroop task. However, control participants were also able to consistently report the trials that felt harder to them, those that took them longer to make a response. Though RMB responded more slowly on some trials and faster on others with the same frequency as controls, she was unable to say which trials were subjectively harder and which were easier. Further, the emotional response of controls to difficulty was evident in a measure called galvanic skin response, which is basically the sweatiness of their hands during harder trials. RMB showed no such emotional response. So, reminiscent of Thorndike’s distinction between effort and efficacy, RMB performed with the same efficacy as controls, but owing to her damaged anterior cingulate, she did not experience the correlated experience of effort.

A fascinating study in 2013 provided some further insight into the subjective experiences associated with anterior cingulate activity.[48] This study was conducted on two neurosurgery patients undergoing an operation for epilepsy. As is common in these surgeries, the patients were awake during the procedure. The brain has no pain or touch-sensitive neurons itself, and so patients do not experience any painful or direct sensory experiences of the surgeon operating on their brain. However, as the patient is awake, the surgeon can electrically stimulate the cortex during surgery. This stimulation can induce mental experiences in the patient based on the function of the stimulated cells. In this study, the surgeons directly stimulated anterior cingulate, in the absence of response conflict or prediction errors, and documented the resulting effects on the patients’ mental experiences.

While they received volleys of electrical stimulation to the anterior cingulate cortex, the patients described an onset of feelings of worry or foreboding, along with an enhanced need to overcome some vague future adversity. These feelings ceased when the stimulation was turned off or when fake, “sham” stimulation was applied or when other, nearby areas of the brain were stimulated.

One patient described these feelings metaphorically. “Like, almost like you’re headed towards a storm that’s on the other side, maybe a couple of miles away, and you’ve got to get across the hill and all of a sudden you’re sitting there going how am I going to get over that, through that? […] That type of a, you know, feeling you have. You’re like, you’re like (pats chest) am I gonna, am I gonna to get through this?” Stimulation of anterior cingulate gave rise not just to an experience of mental effort but a feeling that one must persevere to succeed. This would surely be a useful contextual input to the brain’s systems for gating and control.

In summary, though undoubtedly central to control, the role of the anterior cingulate remains somewhat mysterious. A consistent set of findings suggests that the cingulate is important for making predictions about the responses and outcomes that can be expected given the current context and action plan, and then monitoring whether those predictions are realized. These signals may not be necessary or sufficient for control per se, such as by obligatorily feeding back into prefrontal systems to increase the intensity of control. But they may yield an experience of effort with a task that acts as a context and motivational drive the control system can use.

To get a sense of what this means in real-world terms, imagine a multitasking situation of the kind Turkers dislike. For example, perhaps we are working on our taxes while also periodically checking email because we are handling an urgent situation at work. This situation is challenging for our control system to manage, and we will be a little less efficient and more error prone on both tasks because of the inherent conflict and interference in multitasking. However, it is also the case that multiple responses and outcomes will be predicted by the anterior cingulate as we work our way through various forms 1040 and Schedules B and D, all the while seeing new email notifications appear. Coactivation of all these possible response pathways and outcomes in anterior cingulate yields a feeling of mental effort. That experience is unpleasant and something we want to avoid unless it’s needed. It is the cost of doing these tasks requiring control. It might lead to our focusing more on one of the tasks to push through or, indeed, avoiding the multitasking situation altogether.

How might these ideas relate to the concepts of value discussed earlier in the chapter? A recent theoretical framework called the “Expected Value of Control Theory,” proposed by my colleague at Brown Amitai Shenhav, along with Jon Cohen and Matt Botvinick, addresses this relationship in an intriguing way. This theory proposes that anterior cingulate has access not only to the costs of cognitive control but also the benefits.[49] In other words, it can weigh the expected gains from deploying control against the mental effort required to enact it. This cost-benefit analysis would, in turn, modulate the engagement of control by lateral prefrontal systems to be efficient in achieving our goals with the right level of mental effort. We would regulate our level of attention to our tax forms versus our email to the degree that the payoff—that is, finishing our taxes or resolving that work situation or both—was sufficiently valuable. This theory is still in the process of being developed to make specific predictions about brain function, including how this cost-benefit analysis is expressed in the anterior cingulate cortex neuronal computations. Nevertheless, it is a promising approach and direction for understanding not just anterior cingulate cortex function but the influence on control by factors related to motivation, effort, and value.

What motivates us to do a task? Obviously, we mostly pursue tasks we think will lead to valuable outcomes for us. For example, we make coffee because we desire that morning fix. We set a meeting at work because it leads to a goal on a project and it fulfills a role we are paid to perform. We fill out a tax form to avoid the consequences of not doing so. Control systems that help us focus and perform tasks are driven by these expectations of a valuable outcome. Thus, when our control fails, it might not simply be a limit on our working memory or a sticky working memory gate. It could also be that we aren’t sufficiently motivated to apply control in that situation.

Importantly, however, as we’ve discussed in this chapter, we don’t expend our mental effort for just any old goal. Our brain does a cost-benefit analysis. We weigh the benefits of success at a task, when aided by cognitive control, against the mental costs of its deployment. The balance on that ledger is what translates into our drive and motivation. Thus, when we find ourselves pulling out a calculator to compute the tip at a restaurant rather than just doing it in our head, it is not a matter of valuing the outcome more or less. It is that we have found a way to reduce demands on our control system while achieving the same outcome that makes the calculator task have a higher net value that the in-the-head version. In other words, cognitive control is motivated both when we use it and, often, when we don’t.

CHAPTER 8

The Information Retrieval Problem

Most of us have a love-hate relationship with our memories. We rely on our memory to hoard troves of knowledge and squirrel away our fondest moments. These remembrances lend a sense of continuity and structure to our experience. Further, there can be no doubt that memory is essential to human intelligence and to effective cognitive control. By this I refer not just to our working memory but also to our long-term memory that stores our experiences and general knowledge even when they have left our consciousness. Indeed, as discussed in chapter 2, our memories give us the power to mentally travel through time itself or to visit hypothetical realities.

However, few people would describe themselves as having a great memory. We are keenly aware of those times when our memories let us down, coming up blank on someone’s name just as we are supposed to introduce them at a party, or struggling in vain to place that song we just know that we know. At these times, the knowledge we seek seems to taunt us, locked away in our memory stores, just out of reach, stubborn and unyielding to our mental exertions.

Our memories seem an even sadder affair when compared with our external memory-storage devices, like smartphones and laptops. Imagine trying to access a contact phone number on your iPhone, and Siri replies sheepishly, “I know that I know that number, but I … well, I just can’t remember it right now. Here’s a random tune I thought of instead.” Indeed, these devices are so superior that most of us outsource our brute-force memorization to them. Whereas I once carried around a large directory of phone numbers in my head, I no longer know any of my friends’ numbers. My phone’s storage is simply faster, easier, and more reliable.

Does my brain have any advantage over my smartphone, beyond the fact that I can’t accidentally leave my head stuck between the couch cushions? Why would the process of evolution have left us with such an unreliable memory? Surely, we aren’t forgetful because of the useful forgetfulness of our ancestors. So, what explains our apparently flawed memory system?

The answer to these questions starts with recognizing that our memory didn’t really evolve as a storage device, like a computer hard drive. As discussed in chapter 2, we use memory to build models of our world, to predict future outcomes, or to imagine scenarios that might arise. Our memory did not evolve to provide us a record of the past. Rather, it serves our ability to make future decisions. As such, the brain’s memory system is specialized to flexibly inform what we are doing; that is, our memory is adapted to solve what information scientists and database experts call the information retrieval problem.

The information retrieval problem concerns how a system recovers stored information with a value that exceeds the cost of retrieval itself. This problem is not unique to human memory. It confronts any system with a large database that must be queried for information, whether that database is our brain’s lifetime store of accumulated knowledge, the vastness of the Internet, or the collections of a university library.[1] The common problem confronted by all these systems is how to efficiently gain access to high-value information. And the problem gets harder the larger the database.

To illustrate, let’s consider an example. Periodically, one of my childhood friends visits Providence, Rhode Island, where I live. On these occasions, I like to take him out to a restaurant in town. It’s an open secret that Providence punches far above its weight in fine dining, and so sampling the local cuisine is a must for visitors to the city. However, I wouldn’t want to take him to the same restaurant we went to the last time he visited. In my planning, I need to remember where we ate previously. Thus, my memories of my last visit with my friend temporarily acquire value with respect to this particular task. For example, a memory of what we talked about at dinner or what he was wearing would be of less value to my task than a memory of what we ate or, indeed, where we ate it.

In this example, the relative value of information recovered from memory is clear; it is any memory that answers my problem of where to eat. But there are also costs to consider when searching for high-value memories, which might not be as obvious. For example, one way to recover my target memory might be to retrieve every memory I have of my friend and then rank each according to its value for my task. Once everything is ranked, I simply act on the most valuable memory.

The problem is that we have been friends since we were in middle school together. We played in a band together. We have been close to this day. That is a lifetime of cherished memories. The vast majority of them, however, have very little value for my current task. Even if I could recover every memory of my friend, I would be wasting a lot of time trying to rank them all to find the best one. This would be analogous to checking out every single book in the library to find one specifically concerning the impact of US statutory law on free speech over the Internet.[2] Whatever value might be gained by retrieving the target information would be far outweighed by the costs in search time and checking each item. If I took this approach to remembering the last restaurant we visited, my friend would likely come and go from Providence while I was still sitting in my armchair, sorting through all my memories and ranking their usefulness.

Thus, any retrieval attempt yields both benefits and costs. Costs can be incurred by the time spent searching my memory or from retrieval failures, such as recovering the wrong information. In the context of the information-retrieval problem, the risk of these failures is that they subvert our goals and result in negative outcomes. For example, imagine that I incorrectly remember bringing my friend to one of my two favorite restaurants. Paradoxically, this might make it more likely that I take him back to the same place I took him before than if I hadn’t remembered anything at all. Further, beyond the cost of time and error, difficult acts of retrieval are also felt as an aversive experience of cognitive effort, much like other kinds of intense cognition discussed in chapter 7. That experience is itself a cost. So, minimizing costs is as much a part of the retrieval equation as maximizing the benefits.

John Anderson is the Richard King Mellon Professor of Psychology and Computer Science at Carnegie Mellon University. He has made numerous contributions to our understanding of higher cognition, from mathematical problem solving to memory. In a series of seminal papers, Anderson emphasized that human memory may be adapted to address the information retrieval problem.[3] According to Anderson’s analysis, there are at least two basic components to consider when judging the likelihood of successful retrieval in a system optimized to solving information-retrieval.

First, the system should be sensitive to how frequently a stored item has been useful in the past. The more a memory has been useful before, the safer the bet that it will be useful again. Imagine you are a librarian deciding which books to keep in the stacks and which to send to the archives off-site. One clue would be how often a book is checked out. Books that were frequently checked out were likely useful and also will tend to be checked out again in the future. So, keeping these books available in the stacks is a good strategy. Use predicts future use.

Our memories can use the same trick. Items we have already retrieved are given some priority for retrieval in the future, and the more that we remember them, the greater the likelihood we will retrieve them again. For this reason, one of the best ways to commit something firmly to memory is to practice retrieving it. With every successful retrieval, it is better lodged in memory and more available to be retrieved in the future.

The second factor Anderson highlighted is the context of retrieval—in other words, the setting, the task, and whatever mental and bodily states we experience when we retrieve an item. If a memory has been retrieved in a particular context previously, such as while in a particular place or while performing a particular task or while feeling a particular way or all these together, then your memory system assumes this memory will be useful in that context again.

For example, if you have an anecdote that got you some laughs at parties lately, you are likely to remember that story in other social occasions and might tell it again (to the chagrin of your partner attending these events with you). However, this story is less likely to leap to mind in other contexts, such as while giving a presentation at work or buying groceries. Indeed, some memories are so context specific that we have a hard time remembering them at all unless we are in the same set of circumstance in which we usually think about them.

Our memories are tied to context because human memory retrieval is heavily dependent on the quality of the cues we have available. Cues can be anything: a smell, a sound, a particular object, a thought, or even another memory. Once active in mind, these cues will elicit retrieval of a memory associated with that cue. From an information-retrieval perspective, this is your brain’s bet about the information that will be useful in this context. The better and more specific your cues, the more readily the associated memory will come to mind. In this way, your context can control memory retrieval and make information remembered in that previous context available to you again.

Putting this all together, as we conduct ourselves around our world, busily performing our various errands, our brain’s memory system is also busily laying down memories based on where and how frequently we use them. Our retrieval system later guesses which knowledge is useful given our particular history of retrieval and the various cues available to it. As a result, more often than not, the information we need in a given situation appears effortlessly at our fingertips based on whatever task or goal occupies us. We mostly don’t even experience this retrieval as effortful; it occurs automatically.

No artificial system comes close to this efficiency. Smartphone attempts to do this kind of automatic information retrieval are ham-handed by comparison. Presently, they mostly give us unsolicited directions and travel times to our next appointment or guess at the likely finish to a word or phrase, with mixed success. Our brain is superior, because, unlike a smartphone, our brain has internal, immediate access to our goals, plans, and thoughts while we do a task. It can use some or all this internal information as a context cue for retrieval. Thus, our memory can marry information retrieval directly to the tasks we are doing dynamically, as they progress.

Of course, we are not entirely dependent on automatic retrieval. We don’t have to wander around hoping we bump into the right cue in order to retrieve information we need. Rather, when information we need doesn’t immediately leap to mind, we are also able to internally direct our retrieval to the information we need for whatever task prevails. Cognitive control is essential for this kind of remembering. In broad strokes, control aids information retrieval both by making recovery of useful information more likely and minimizing the costs associated with retrieving it.

In this chapter, we will take a close look at cognitive control of memory. As we will see, control over memory retrieval can be roughly divided into two basic categories: those control processes that shape retrieval itself, which we will term controlled retrieval, and those that operate after information has been retrieved, which we will term postretrieval control. In the next sections, I will define these categories and give some examples of each and how they contribute to information retrieval. We will then explore what brain systems support these categories of memory control, how the brain assigns values to memories, and what these features might mean for thinking about our own memories.

Controlling Retrieval

To understand how cognitive control influences retrieval, it is helpful to draw an analogy to an artificial retrieval control process with which we are all familiar: using a web search engine, like Google.

Google is designed to address the information-retrieval problem using established principles from cognitive science. Although its specific algorithm has evolved over the years, the basic principle behind a Google search is its PageRank system. When you enter a search term into Google, PageRank gives an authority score to webpages listing that term based on the number of pages that link to it, weighted by the authority score of the pages doing the linking. In other words, if you were looking for a website on the effect of concussion on the brain, a link from a page that itself has many links, like BrainFacts.org, counts more toward a site’s PageRank authority than a link from a site with only few links. So, some amateur blog post on concussion read only by the blogger’s parents probably won’t even make your browser window. The top pages in a Google search, then, will be those linked by lots of other high-authority sites, similarly defined.

At its core, then, PageRank follows the heuristic that use is a good indicator of future value. If multiple webpages find a given page to be useful enough to link, then that webpage is likely to be useful in the future and should be ranked higher in the search. There are some additional tweaks built into its system, such as giving a bonus to conventional authorities, like government or university webpages. But apart from these exceptions, the simple use-based system works quite well. Indeed, the top page of a Google search is often precisely what you are looking for.

Even with a sophisticated search engine like Google, however, it is important that you develop a good set of search terms to enter into Google’s query field. Imagine, for example, that you want to find my lab website. It’s helpful that my name is unique. Badre (pronounced “better”) is a French transliteration of a Lebanese name. It’s a rare spelling, even in my own family; many of my relatives transliterate it as Badr instead. Of the few e-carrying Badres out there, I know of only one other with the first name David, somewhere in France, I think. So, it’s likely that if you enter just my name into Google, you will get all the information there is out there about me.

Now imagine, in contrast, that you wanted to look up my colleague Michael Frank at Brown, whom we first met in chapter 3. His name is basically Google proof, made up of two common first names. And to further complicate matters, there is another Michael Frank, at Stanford, who is also a computational cognitive scientist! So, to find out information about the Michael Frank at Brown, you might need to generate a more specialized query, such as entering “Michael Frank Brown.”

Anyone experienced with browsing the web is used to this kind of task and probably does it without a second thought. In fact, whereas some of you might have entered “Badre” in a search for me and found me with only a little browser scrolling, probably no one would enter just “Frank” for Michael. Thus, you are using some knowledge you already have about Michael—that he works at Brown—to generate your query to get more specific knowledge. This is a retrieval plan that controls retrieval and makes it more likely you’ll get to the information you need. Good retrieval plans will lead to good retrieval outcomes.

Returning to human memory, we don’t have to stop what we are doing and enter a query into our brain’s search field to get the information we want. Again, this is one of the advantages that human memory has over an artificial system like Google. Merely perceiving, thinking about, or processing some information essentially submits it as a query to memory automatically, and, accordingly, our memory obliges us with some associated information. Importantly, however, when the cues available to us in our conduct through the world don’t automatically retrieve what we need, we can generate a retrieval plan to control retrieval. And, as with our Google query, we can do so using our existing knowledge to get to more specific and useful knowledge.

For example, the information I need about an elephant is very different depending on whether I want to describe one to my child or am considering riding one. If I want to ride an elephant, the information I most strongly associate with an elephant, such as that they are huge and have trunks, is probably less relevant for my task than what I know about their disposition and the places I can find one to ride. So, I want to shape the search to make retrieval of these other elephant-riding features more likely. I need a retrieval plan that involves a process of retrieving information as a cue for more specific information.

One of the clearest examples of how people generate plans for retrieval is during tests of cognitive estimation. These tests ask people to make a good guess at questions they likely have never heard before. For example, what would you estimate the length of the average human male spine to be?

Do you have an answer? Assuming you aren’t a spine expert who knows this fact already, what approach did you take to answer this question? Everyone has their own approach, but mine would be as follows. I would start with the average human male height which I know to be around 5 feet 6 inches. Then, I know that our spines don’t reach all the way from the top of our head down to our ankles, so I subtract around 3 feet for the legs and half a foot or so for the head, and voilà, I arrive at a guess of just over 2 feet for the spine. This estimate is close to the actual number, which Google tells me is 2 feet 4 inches (71 centimeters). Pretty good for a fact I’ve never explicitly learned.

We solve these kinds of problems routinely all the time. You can see that doing so requires not only recalling facts we already know, like the average human height, but also requires a retrieval plan, a progress of thought, that selects the bits of knowledge over which to compute a result.

Thus, just as one may need to control a sequence of behaviors, one can also helpfully control a stream of thought from memory. Patients with cognitive control deficits have a hard time with this task.[4] They may be able to recite facts they know, like the average human height, but when presented with a question like the length of the male spine, for which they have no memorized answer, they struggle to generate a reasonable response. Rather, they will tend to give a response based only on what they can immediately retrieve, like responding with the average height of a human male.

Retrieval plans do not just apply just to estimation; we also use them to retrieve specific facts, details, or experiences we have already stored in memory. For example, imagine I asked you to list all 50 US states. How would you go about this task? If you are from the US, this is not asking you to estimate a new fact, as you’ve know the names of the states since grade school. But coming up with them all is a hard memory task. Just saying whatever states come to mind will get you some of the way, but you will likely run out of steam before getting all 50. Rather, most people come up with some type of plan to organize and guide their retrieval. For instance, people might try to go through the alphabet thinking of every state that begins with a particular letter, or they will work geographically, crisscrossing a mental map of the country as they generate names.

Regardless of the specifics, what retrieval plans have in common is that they use your existing knowledge, like the schemas discussed in chapter 2, to guide retrieval. Like adding “Brown” to your Michael Frank query, you are using some knowledge as a query for getting to more knowledge. The memory system is playing with what you know, chewing on it, and elaborating it to make retrieval of useful information more likely.

One of the most sophisticated and effective organizational strategies for memory is the “method of loci” or “mind palace.” Perhaps contrary to current belief, this technique was not invented by Sherlock Holmes in the eponymous hit BBC series starring Benedict Cumberbatch; the technique is actually quite ancient. It was supposedly introduced by the Roman statesman and orator Cicero, who credited it to the poet Simonides of Ceos. The story goes that Simonides was the sole survivor of a dinner party in which all the other guests were burned beyond recognition owing to some terrific calamity. To identify which guests attended, Simonides mentally “walked” around the table in his memory of the dinner party to remember each person based on where they were seated.

According to memory historian Mary Carruthers at New York University, the method of loci was refined to a highly developed skill by medieval scholars.[5] Indeed, mastering it was a fundamental part of academic training in the Middle Ages. For example, a scholar might make only one visit to the Vatican library during an entire career and have only that chance to read a particular text, so it was crucial to commit that text to memory verbatim. Thus, mastery of mnemonic devices like the method of loci was essential to a successful academic career. In fact, a superior memory was admired as a sign of genius, much like creativity or originality might be considered and cited in a letter of reference today.

The method itself, like any skill, takes practice to master. You imagine a place you know very well, like your house. You should know it well enough to be able to see its detailed features and layout in your mind’s eye. Then, to memorize any material, you mentally move through the structure and place items to be remembered in different locations throughout the space. For example, you might put each of the 50 states in a different drawer in different rooms throughout your mind palace. Then, when it comes time to retrieve them, you mentally walk through the space, opening those drawers to recover what was placed there.

This technique is remarkably powerful. Champion mnemonists who can perform feats like memorizing 145 random words in five minutes (a world record set by champion mnemonist Yanjaa Wintersoul in 2018) have trained extensively to use it. What they do is staggering, but it is not magic. Rather, it is leveraging our natural ability to aid memory by structuring learning and remembering with our knowledge.

Though most of us don’t walk through memory palaces when we remember, the act of controlling memory retrieval can be similarly structured. By accessing the right knowledge schema and using it to guide retrieval, we walk through our knowledge like a medieval academic wanders through a mind palace. In doing so, we foreground certain knowledge and make it available in mind. Once active, this knowledge can then act as a cue to aid retrieval of associated information we need for whatever task we are performing.

Generating these knowledge structures and guiding ourselves through them requires cognitive control. Thus, it should come as little surprise that patients with frontal lobe damage have difficulty applying organizational strategies during study or retrieval. Berkeley cognitive neuroscientist Art Shimamura conducted a number of studies characterizing the memory deficits of patients with frontal lobe damage.[6] In one such study, patients and controls were given lists of unrelated words to memorize and then freely recalled them in any order they liked during a later test. The controls frequently used a number of organizational strategies to help them learn and then remember the words, like forming a story with the words. Frontal patients used strategies like forming a story far less frequently, only around a third of the time. Instead, the patients were more likely to just close their eyes during retrieval or to use no strategy at all.

What we learn from studying patients like those in Shimamura’s study is that applying retrieval plans doesn’t occur spontaneously. It is an active process that requires control systems supported by the prefrontal cortex. However, the role of cognitive control may not simply be in executing the organizational strategy itself, such as by forming a story or walking through a mental structure. Rather, control systems may be important for evaluating whether a strategy has added value and whether it is time to shift to something different. This requires a cost-benefit analysis at the heart of the information-retrieval problem. In other words, what is the value in deploying the current strategy versus shifting to a new one? In this sense, retrieval can be thought of as another foraging problem. Like our animal from chapter 7 searching for berries, one has to decide when and for how long to probe memory for more information using a particular retrieval plan, and when it is time to try a new one.

Consider, for example, the basic free-recall task we described earlier. Often, when people are given a list of words to remember, the words come from different categories. There might be animals, tools, and buildings on the list, for instance. At retrieval, those categories can be useful for guiding retrieval. Thus, a common observation is that when people remember the words in any order they choose, they will recall them in groups or clusters based on these categories regardless of the order they saw them during study.[7]

Assuming you are remembering items this way, how long should you remain generating items from one category before switching to a new one? This is akin to the foraging problem in that each category is like a bush for harvesting remembered words rather than berries. You need to decide when word-berries are sufficiently depleted in one category that you should switch to a new, more fruitful category.

Evidence from patients suggests that frontal cortex is needed for this kind of decision. Though their clustering scores are lower than those of controls, patients with damage to prefrontal cortex will show some degree of clustering during free recall.[8] Importantly, however, the patients will tend to stay with one category longer than controls do, likely past the point where doing so is useful. As a consequence, the patients generate fewer items and make more errors, like coming up with a word that wasn’t on the study list or saying the same word twice.[9] But their reduced recall is not necessarily because of amnesia per se. It is at least partly because they were unable to balance the diminishing returns for a given category against the rising costs of retrieval in order to shift appropriately.

It is clear, then, that people are able to assess how effectively retrieval is going and then can shift to new retrieval strategies accordingly, using different knowledge to probe memory in new ways. It is also the case that people use this knowledge to decide whether it is worth exerting mental effort to try to retrieve specific information from memory at all.

To illustrate, imagine that you read the original Grimm brothers’ version of “Little Red Riding Hood.” If I later ask you whether Red brought her grandmother a can of energy drink in her basket of treats in that story, you can do one of two things. You might try to remember the line in the story you read describing the specific contents of her basket. If you correctly remember that in this version she brings her grandmother a piece of cake and a bottle of wine,[10] then you can say no to my question based on this direct retrieval. But if you can’t easily remember that specific line, then you might just make a plausibility judgment. You can easily remember that Red brought her grandmother treats. You know that an energy drink is not a likely treat for the setting of the story. So, because it is implausible, you answer no, even though you don’t directly remember what was specifically in the basket. In this example, rather than directly remembering the story, you remember what you can easily—that this story comes from a time before energy drinks—and then come up with the answer using logical inference. So, in the energy-drink case, making the plausibility judgment might be easier than actually remembering one specific line in the story.

In a similar vein, Lynn Reder of Carnegie Mellon University found that when confronted with questions that could be answered by directly remembering a passage from the story, people would nevertheless occasionally forgo retrieving the fact and, instead, just judge whether it sounded like a plausible part of the story.[11] She found that the choice of strategy depended on how easily the relevant information could be retrieved. So, in our example, if someone did not learn the contents of the basket well, making it hard to remember, they would be more likely to make a plausibility judgment.

Of course, making a plausibility judgment also risks error. Imagine that instead of an energy drink, I asked whether Red brought her grandmother a muffin and some cookies. Plausibility might lead you astray here and drive you to incorrectly say yes. Indeed, until I looked at the actual story, I would never have guessed a bottle of wine was among the treats. Thus, people don’t always go with plausibility. If Reder’s volunteers knew the probed detail well enough, they would use direct retrieval and be more likely to be correct. Thus, in each case, people naturally balanced the cost of difficult retrieval against the benefit of a lower likelihood of error.

It follows from this discussion that our control system must have access to some information about how well we know something or even how likely we are to remember or know something in the future. This is sometimes called meta-memory, as it refers to our knowledge of the state of our own knowledge, and it is used to influence our retrieval by providing our memory system cues and inputs that we derive from knowledge itself. By elaborating or thinking about a concept, that concept becomes a cue and shapes how we retrieve, analogous to a query in a Google search. We weigh the likelihood of success against the effort required to recover an item and the risk of taking another, easier approach. Thus, when we are remembering, the determining factor is not the reliability of storage but rather the optimal balance between the benefits of success and costs of ensuring that success.

Control and the Products of Retrieval

Control of retrieval is not the only way that we can manage the costs and benefits of memory. We also have a suite of operations we can perform once retrieval has happened in order to make the most of what has been recovered. We will collectively refer to these as postretrieval control processes, and they are just as essential to successful information retrieval as controlled retrieval.

To illustrate, let’s return to our example of the Google search. Even with well-chosen search terms, you will need to do some additional work to get to the webpage you want anytime you search, because, no matter what the search, you will likely retrieve more than one website. Indeed, even in the early days of Google, it was so hard to submit a query with only one return that a nerdy pastime sprang up called “Googlewhacking.” The goal of Googlewhacking was simple: enter two keywords into Google without quotation marks that return exactly one website. People who sifted through the thousands of word pairs necessary to find a search that qualified could ascend to Internet Valhalla by having their name and search term posted on the Googlewhacking website. Quite an honor, indeed.

If Googlewhacking teaches us anything—and it surely has taught us so much—it is that relying on only your search terms to retrieve exactly the target memory you want is incredibly hard. In almost every case, more work is needed to choose the website you want to use from the returned list. Yes, sometimes the website you want is the first one listed, but often it’s not. That’s why no one uses that “I’m feeling lucky” button Google provides to take you past the browser window and directly to the top site on a search. In 2007, Google estimated that 1% of searches went through that button, and I’d wager that they were rounding up.[12]

This problem is not unique to Google. It is in the nature of any large, distributed memory system to routinely produce lots of irrelevant results along with relevant ones in response to a query. So, some form of postretrieval monitoring and selection is almost always required.

Human memory is no exception to this rule. We will remember information of varying utility for our goals, and given the constructive nature of memory, also of varying quality. We need to filter and monitor what we retrieve with respect to our goals and our conception of the world. Failing to do so can lead to various memory problems, including confabulation, discussed in chapter 2. Further, just as the physical limits of our browser window constrain how many site summaries we can see at once, we have a limited working memory capacity in which to hold the products of retrieval. As a consequence, we must limit what information from long-term memory is allowed into working memory.

In general, then, we need control that occurs postretrieval whenever we decide to act on remembered knowledge. That action might be to make a response, to engage in some more retrieval, or to hang on to that retrieved knowledge in working memory. In this sense, then, postretrieval control can be conceptualized as closely related to the gating processes we have discussed to this point. However, rather than gating information into working memory from our perceptual systems, in this case, we are gating in information arriving from long-term memory.

To illustrate, one of simplest everyday examples of postretrieval control concerns the control over so-called output grain, or the level of detail we select from memory.[13] When you remember a prior event, your memory is going to provide lots and lots of data, both general and detailed. A skilled storyteller will pick out just those bits needed to understand the narrative and leave out the unnecessary detail. A less controlled telling will just provide the details as they come, no matter how big or small, all treated with equal weight. It is a situation to be ardently avoided at a dinner party. When we pick the right details, we have controlled output grain.

Control over output grain is another effective way to manage costs and benefits of retrieval, though here postretrieval. Though you might convey more information in a highly detailed report, you also risk making errors. If I ask my kids about an argument they had a week ago, they will give me the gist of what happened. It was in the afternoon. They had trouble taking turns. If I want more detail, I will need to probe for it. What time exactly? What was said? Who did what to whom? As these questions get detailed, I will get increasing uncertainty in their remembrances, and of course, increasingly disparate accounts. This makes sense, as details are generally harder to remember than the gist of a prior event, so the gist is probably more accurate, even though the details may be more informative.

People routinely balance this trade-off between accuracy and informativeness in their reports.[14] To do so, they make use of several contextual signals. They are sensitive to the factors in a situation that favor accuracy; for example, are they telling a story casually to a friend or providing testimony in court? They also have access to meta-cognitive signals about the strength and reliability of the evidence they are recovering from memory. Using these signals will determine how effective a person is in controlling their grain size and so balancing informativeness with accuracy.

Not surprisingly, output grain control is characteristically hard for patients with frontal lobe damage. Brickner’s 1936 account of Patient A provides several illustrative examples. As Brickner described it, “A striking example of intellectual malfunction is A’s particularization. Frequently, all details seem to be of equal importance, and in the narration of a story, for example, few are omitted.”[15]

Consider this instance, when A is asked by one of his doctors what he did that morning.

I woke up about five o’clock this morning—four-thirty or five—and I stayed awake until I got out of bed about twenty minutes of eight or half-past seven. Then I got out of bed and I came to the washroom—took some Listerine and washed my mouth. After that I let the hot water run and got a piece of soap and washed my hands. Then I walked into this room and saw my dad and mother reading their papers. I did not wish to disturb them, so I walked to my own room—yosha de bosha. Then I walked into Mrs. Adams’ [his wife] room and she didn’t want to be disturbed until eight o’clock, but that didn’t stop me; I walked into that room and she was sort of asleep—she appeared to be—I don’t know whether she was; she gave that impression. You know girls fool around in so many ways you don’t know. I made a little disturbance, but I made enough noise so that she woke up. Well, I didn’t see her last night. She was probably playing bridge while I was playing poker. I’m pretty sure I didn’t see her last night.[16]

Most of us would have responded simply that we woke up early, got dressed, and then because we were bored or missed them, we roused our spouse before they were ready. However, these key points are lost amidst details about what brand of mouthwash he used. Anyone who has spent much time with small children will have seen that their stories have a similar charm. Again, this is not a memory problem per se; it is a control problem. It is a failure to identify and choose those details that have the highest utility, in terms of the balance between informativeness and accuracy.

Postretrieval control is also a prime means by which we offset some of the major causes of forgetting. We often think of forgetting as a decay process. Information gets put into memory, and then like segments going bad on an old hard drive or a picture fading in time, our memory just corrodes and deteriorates. However, this folk theory of forgetting is not quite right. Rather than being a passive time-dependent decay process, much of forgetting can be explained by active interference from other memories that block and corrupt the target memory.[17]

For example, has it ever happened to you that you are trying to remember a particular song, but the wrong song comes to mind? This always happens to me when I try to remember the theme song for the old Superman movie series starring Christopher Reeve. When I was a small child, I was enamored by the 1980 classic, Superman II, and I remember loving the theme song. But, whenever I try to remember that song as an adult, I think of the Star Wars theme instead. At that point, it’s hopeless. I am simply unable to remember the Superman theme.

The Superman theme is still in my head somewhere, however; I haven’t lost it. If I go find it on Spotify, I recognize it instantly; the main motif springs to mind almost as soon as the first horn plays its note. But when I try to remember it on my own, another memory interferes. When this happens to us, the imposter confounds us. It blocks our memory until it goes away or we can suppress it in some way. Sometimes this happens when enough time passes that the blocking memory diminishes in mind and the memory we wanted comes to mind, spontaneously. Unfortunately, this often occurs when we no longer need the memory or care about it. Thus, we can also try to use postretrieval control mechanisms to select the correct memory over the interfering memory, akin to how we might support the color-naming pathway over the word-reading pathway.

It follows that failures of postretrieval control can lead to greater interference and more forgetting. Take, for example, the forgetfulness that comes with old age. There are many causes for memory problems in older adults, but changes in postretrieval control over interference are one of them. For example, a former postdoctoral fellow in my lab, Ilke Öztekin, investigated this phenomenon directly in a group of older adults.[18] In Ilke’s study, adults between 60 and 74 years old were compared with younger adults, ages 18 to 26. Both groups were repeatedly given sets of three words to remember and then a test word. For each test, they were to decide whether the test word matched one of the three words they had just seen. They would do this cycle repeatedly—receive a set of three words, get tested, and then receive three more words to remember.

Without telling our volunteers, Ilke manipulated some of the test words to induce competition. Specifically, she included foil words that were not in the current study set but that had been in previous study sets. This arrangement drives a tendency in people of all ages to say they remember these foil words as members of the current study set, even though they are not.

Presumably, these false memories are because of interference. When those words appear, they feel familiar because they had just been seen recently. This familiarity pulls at us to say we remember seeing them in the study set; however, to answer correctly, we must figure out that they were not really part of the current set. In other words, we monitor what our memory system produces for quality assurance. When people are given enough time for this postretrieval monitoring, they will correctly reject these foil words.

Older adults will incorrectly report memory for the foil words at a higher rate than younger adults, as they are more influenced by the effect of competition. But why? Through some clever manipulations of the timing of her memory tests, Ilke observed that older adults are slower to start engaging in postretrieval control than the younger adults. As a result, that feeling of familiarity from memory has time to build up and become stronger in the older group. That strength translates into higher rates of false memory.

Thus, memory problems in old age are not always about losing information from our memory stores, but rather, they sometimes arise from the control processes needed to guide memory retrieval and resolve interference. In the case of Ilke’s experiment, memory failures were specifically attributable to differences in postretrieval monitoring. As we will discuss further in chapter 9, cognitive control changes as we age, and so these changes are a contributor to the problem with memory we experience in aging.

In our daily lives, we routinely shift around our postretrieval evaluation of our memories based on our current situation. Take for example the case of running into someone who is familiar to us, but we can’t recollect where we encountered that person previously. In these kinds of situations, memory is providing some evidence of prior knowledge, but it is not without uncertainty. We know we can experience a feeling of familiarity for something entirely new. Indeed, we can even experience déjà vu, which might be defined as a feeling of familiarity in a situation we know we cannot have experienced before. Postretrieval control needs to help us evaluate that evidence from memory in order to act.

Importantly, the decision of whether to act or not has consequences. Maybe that person just looks a lot like someone else I know. There are real social costs to waving enthusiastically at a complete stranger. However, if I have met this person previously and just walk by them without acknowledgment, the perceived snub might be just as costly.

Thus, when confronted with some evidence from memory, I have to make a decision about how to act on it. My decision will be at some level of uncertainty depending on the strength of the evidence from memory, and it must be balanced against the various costs and benefits of the action I will take. It would stand to reason that if I have a very strong feeling that I know this person, then I should be more likely to take the chance of saying hello. Conversely, a very weak sense of familiarity might lead me to move on.

In essence, I need to set a threshold for the evidence from memory. If the strength of familiarity exceeds that internal threshold, then I will treat this person as someone I know, and if does not, I will treat them as a stranger. Further, it is important to set that threshold flexibly depending on circumstances. For example, I might have a very different threshold depending on whether I am at a scientific meeting where I am highly likely to run into people I already know, versus touring with my family at a remote site in India.

Laboratory studies have shown that people adjust their memory thresholds depending on what they know about the likelihood of encountering old or new items during a memory test. If people know it is unlikely they will encounter items they have seen before,[19] or they are instructed to avoid saying they recognize an item that was not actually presented,[20] then they will adjust their thresholds to be more conservative. Conversely, increasing the frequency of words they have seen before will make people more liberal, saying they recognize more words at test, even for words they did not actually study.

In these two cases, people’s memories are the same; they had no knowledge during study of the conditions at the test. It is what people do with the evidence retrieved from memory that differs based on the context. They adjust their decision threshold to balance the likelihood of correctly identifying old items against the costs of an error. Postretrieval control is how they do this.

In sum, control can affect memory in two ways in the service of information retrieval. By leveraging existing knowledge and foregrounding certain information as a cue, we can use controlled retrieval to make recovery of more useful information likely. Regardless of what our memory produces, however, we also engage in control postretrieval to ensure we act on those memories that are most useful given our goals. How, then, are these processes supported in the brain? That’s the topic of our next section.

Two Brain Systems for the Cognitive Control of Memory

Controlled retrieval and postretrieval control are not independent. Rather, these processes necessarily trade off. Consider once more the analogy to a search engine. The better the quality of search terms you use in your query, the less junk will be retrieved into your browser window and so the less time you will spend sifting through it all to locate what you want. Conversely, if it is hard to generate a good query, then postretrieval processes can pick up the slack by selecting relevant information and suppressing the irrelevant.

However, this distinction between pre- and postretrieval is not merely descriptive. Cognitive neuroscience research suggests that the brain divides labor along these lines, devoting separate systems to pre- and postretrieval control.

Neuropsychologists have long observed that control systems are important for memory function. We have already discussed examples of frontal patients’ deficits in the control of memory, such as using organizational strategies or adjusting output grain. In broad terms, patients with general problems in cognitive control will also show problems in memory in cases where the retrieval problem is difficult or when they must use their memories to solve new problems.

Although memory control problems could arise from breakdowns in any of the control systems we have discussed to this point, it has also long been an observation that patients with damage to the left ventrolateral prefrontal cortex, shown in Figure 8.1, are particularly susceptible to memory control problems.[21] This general observation has led to a long debate about the role this region plays in the control of memory.

Early evidence suggested that patients with damage to left ventrolateral prefrontal cortex had problems with controlled retrieval, specifically. For example, these patients would show severe deficits in production tasks that rely on long-term memory. One such test is the verb generation task.[22] In this test, the patients are given a noun as a cue, such as the word “ball,” to which they must generate as many verbs as they can that relate to that noun, like kick, throw, juggle, or roll. Patients with damage to the left ventrolateral prefrontal cortex typically perform worse at this kind of task, producing fewer correct responses than do controls. The conventional interpretation of this deficit was that these patients suffered from a problem with controlled retrieval; that is, they were unable to use their knowledge of the noun to direct retrieval to the relevant verbs.

A foundational study by cognitive neuroscientist Sharon Thompson-Schill called this interpretation into question.[23] In this study, Thompson-Schill chose one set of noun cues with many associated verbs, like “ball,” and another set of nouns with only a few associated verbs, like “book.” You might predict that patients with a memory-retrieval problem would have trouble in the latter case, because they are searching for these rarer associate verbs. But the patients with damage to the left ventrolateral prefrontal cortex had the opposite problem: they had a hard time producing responses when there were many associates. So, rather than having an issue retrieving, their problem was picking out the verbs to report from many competing alternatives. That is better described as a postretrieval control process.

FIGURE 8.1. Drawing of the left lateral and medial surfaces of the brain with left ventrolateral prefrontal cortex (VLPFC), ventral medial prefrontal cortex, lateral temporal, medial temporal lobes labeled.

The study from Thompson-Schill touched off a minor, though lively, debate in the cognitive neuroscience literature about how best to characterize the role of the left ventrolateral prefrontal cortex in cognitive control of memory. Was it supporting controlled retrieval or postretrieval control or maybe both? I, too, joined this debate as a young scientist and devoted my doctoral research to this topic, which I developed into a theory of prefrontal contributions to control of memory.[24] However, it was not until the field moved beyond the idea of individual brain regions that have specific localized functions and toward brain networks that compute through their connections that a clearer picture began to emerge.

Years after this debate had largely died down, my doctoral student Jennifer Barredo and I returned to the memory control problem, but this time considering the networks that are important for these different modes of control. Jennifer came up with a memory task that allowed her to manipulate both controlled retrieval and postretrieval control in the same experiment. This way she could compare the two directly.[25] Further, rather than focusing just on the left ventrolateral prefrontal cortex, she studied the broader brain networks related to controlled retrieval and postretrieval control.

Using this approach, Jennifer discovered that the ventrolateral prefrontal cortex is engaged by both forms of control, consistent with the prior reports. However, the broader networks engaged by these demands are entirely different, as shown in Figure 8.2. Controlled retrieval engages a ventral frontotemporal network that connects the frontal lobe with areas of the temporal lobe important for memory, like the hippocampus. In contrast, postretrieval processing engages a dorsal network that closely matches the frontoparietal control system discussed to this point.

In summary, then, the brain has at least two systems for controlling memory. Postretrieval control is supported by the general frontoparietal control system. In fact, it is reasonable to hypothesize that it relies on working memory gating mechanisms to carry out its function. Specifically, as information is recovered from memory, those memories can be updated to working memory. Once there, they can influence responses through the input and output gating system in accord with their value.

Controlling retrieval relies on a distinct network from the frontoparietal system, though we do not yet have much data on how its mechanisms work. One reasonable hypothesis is that it also relies on a working memory process similar to the one we have posited for other kinds of control. In this case, however, it is the cues and knowledge we use to query long-term memory that are held in working memory rather than a task demand. Holding this information in memory thereby shapes what patterns of activity form in other parts of the brain that store our knowledge.

FIGURE 8.2. Schematic showing how controlled retrieval and postretrieval control systems interact with memory processing. Information enters from the environment and can be modulated by controlled retrieval pathways, shown on the bottom brain. Once a result is returned from memory, postretrieval control systems, shown on the top brain, monitor and select it for decision and action.

Importantly, however, we should not interpret this network difference as indicating that these control functions are entirely independent of each other. As we have already noted, these two memory processes are in a push-pull relationship. If you set up a good retrieval plan or elaborate useful cues, then it is likely that useful information will come to mind. However, if the controlled retrieval system does not operate effectively, such as by yielding more irrelevant information, this will put pressure on postretrieval control. In other words, equilibrium is maintained between memory control processes; it is a homeostatic relationship. One may rely more on one process in one situation or more on the other process in another, but ultimately, memory performance remains stable.

There are profound implications for having two memory control systems that interact in this way. As just one example, consider the case of memory deficits in degenerative disorders like Alzheimer’s disease. According to the Alzheimer’s Association, an estimated 5.8 million Americans had Alzheimer’s disease in 2019, and that number is growing steadily with a growing older adult population.[26] The disease results in slow degeneration in the brain, accompanied by progressive memory loss and dementia. Patients eventually lose memories of their families and closest loved ones, as well as awareness of where they are and what is happening about them. They become disoriented and confused and can no longer live independently. Ultimately, the disease is fatal. It is a tragic process that brings hardships and heartache to both those affected by the disease and their loved ones. A major worldwide effort is underway to better understand the disease, and ultimately, to better treat and cure it.

There is evidence that the uncinate fasciculus and ventral stem white matter connecting the ventrolateral prefrontal cortex and the temporal lobes are among white matter pathways to degrade early in Alzheimer’s disease and are predictive of conversion to the disease.[27] These are the very white matter pathways Jennifer identified in association with controlled retrieval. Why don’t people start exhibiting memory problems earlier on in the disease if these pathways are degenerating?

Nobody knows for sure, but one possible answer could be the homeostatic relationship between the memory control systems. If the ventral controlled retrieval system is less effective, the postretrieval control system will start to pick up the slack to some degree. Over time, the controlled retrieval system might continue to degrade, and so more and more pressure will be placed on the postretrieval system, which itself might start to suffer the effects of degeneration. At some point, a tipping point will be reached, the postretrieval system can no longer compensate, and the full depths of the memory loss will be exposed as a catastrophic loss of memory function.

Importantly, this is only an illustration of how changes to the homeostatic relationship of brain systems could be expressed in a degenerative disease. I am not proposing this as a theory of Alzheimer’s disease, and there is no evidence for these specific dynamic changes in that disease. Rather, I offer this scenario to make concrete the implications of homeostatic memory control systems for aging and disease. Specifically, these dynamics can complicate diagnosis and yield a catastrophic change in cognitive function without a corresponding catastrophic event in the brain, like a stroke or closed-head injury.

The brain is a homeostatic organ with many systems complementing one another. Cognitive control function is a clear example of this, as we have seen in this book. Thus, understanding its function requires understanding how its various subcomponents work together and trade off. Likewise, understanding changes in control function in any major disorder cannot focus on one system or one function. It will likely require understanding how this balance changes with progress of the disease, and that is the frontier of current systems neuroscience research on Alzheimer’s disease.

Value and Memory

A critical topic that we have left out of our discussion of memory control so far is value, though this is central to our framing of memory control in terms of the information-retrieval problem. As we’ve already noted, many external information-retrieval systems rely on use as a proxy for value. However, imagine if they could track value more directly. Imagine if your librarian knew how much you loved a book that you checked out or how much that article helped you write that research paper. Or consider if Google followed up your web query to ask how helpful that website turned out to be. Indeed, what if Google just automatically knew how helpful its searches were? If these systems had this kind of information, then search devices designed to solve information retrieval could learn from their prior retrieval attempts and rank information based on their utility more directly. They would likely become even more effective at providing just what you need when you need it.

Unlike Google or your librarian, your brain’s search engine does have access to this information. When you achieve some measure of success at a task, your brain can assign credit accordingly. It can leverage more than just use; it has access to the goals and outcomes of your actions. This is just one more reason that your internal memory system far surpasses any artificial memory system.

In chapter 7, we discussed how reward prediction errors can lead to increases or decreases in the neurotransmitter dopamine. We also saw how this value information might modulate and serve as a learning signal for cognitive control systems, such as those that gate working memory. In recent years, it has also been observed that dopamine has targets within the brain systems responsible for retrieval from long-term memory, including the hippocampus itself.[28] This signaling means that the retrieval system gets feedback about value, about how things are going, and how things went. It can use that information to modulate memory itself.

Erin Braun, Elliott Wimmer, and Daphna Shohamy provided evidence of this phenomenon in a maze-learning task.[29] Participants in the study would navigate around a maze on a computer screen to find a hidden reward. While wandering around the maze they also encountered various pictures of objects along the way. The objects were incidental and unrelated to the task at hand. Nonetheless, on a memory test 24 hours later, the volunteers had a better memory for the objects that had been closer to the goal than those farther away.

What happened here? People did not know where in the maze reward would occur, and so when they encountered an object in the maze, they did not know whether it was close to the goal. They knew its proximity only once they actually got to the goal. Thus, in the 24 hours following the maze run, the memory system must have reevaluated the moments leading up to the goal and assigned the objects a memory strength based on their value. This made them easier to remember.

What might be signaling the memory system about these rewarding outcomes? The authors of this study speculated that dopamine might be an important neurotransmitter in this kind of memory modulation. As discussed in chapter 7, dopamine increases with prediction of an upcoming reward. Further, research with animal models has shown that dopamine increases may modulate the hippocampus to form new memories.[30] Thus, dopamine might be a way that the brain can prioritize learning as a function of value.[31] The brain doesn’t need to know why things are good right now or if the content of thought is particularly good itself, but when reward signaling increases, the brain will prioritize memory for whatever the content of thought might be at that time or what moments led up to that rewarding state of affairs. This makes that information more likely to be remembered in future.

Harvard clinical psychologists Daniel Dillon and Diego Pizzagalli have proposed that disruptions of this internal prioritization system might be one explanation for memory disturbances experienced by people with clinical depression.[32] Depressed people have been observed to show a negativity bias in their memories. This means they are less likely than healthy people to remember positive events in their lives while being more likely to remember the negative ones.

Dillon and Pizzagalli note that stressful events commonly trigger episodes of depression. Stress also has several effects on the brain that can cause memory problems, one of which is a blunting of dopamine release by the midbrain. If dopamine is less available to modulate hippocampal learning during depressed episodes, it will cause a general loss of that positivity bonus that healthy people enjoy. Thus, putting it all together, when a patient has a depressed episode, there will be less dopamine from the midbrain in response to positive events. Consequently, there will be less dopamine to modulate the hippocampus to remember these events. As a result, depressed patients will lose memory of positive events at a higher rate than a healthy person.

It is not hard to imagine the problems that will follow from tending to forget the positive events in our life more than the negative ones. Indeed, it is a characteristic of depressed persons that they will feel sad or even hopeless even when their situation seems to be more positive than they feel or perceive. Given that memory is central to the model of the world we construct for ourselves, a negativity bias in memory could be a contributor to this characteristic construction of our world. In other words, being able to appropriately value memory doesn’t just ensure that we remember useful things; it shapes our understanding of the world around us.

When I was a child and was frustrated because I’d forgotten something I was going to tell my mother, she used to give me a comforting smile and tell me, “that only means it probably wasn’t important.” Back then, that served only to frustrate me all the more. But, years later, as a scientist who studies cognitive control, I must acknowledge the wisdom in that adage. As we have discussed in this chapter, our memories did not evolve as storage devices, like computer hard drives, but as search engines, like Google. When confronted with a task or goal, we can bring to mind information with high utility for our situation. Multiple, interacting control systems can help by actively generating retrieval plans and reducing interference after we remember. We assign value to memories based on the outcomes of our actions and prioritize them accordingly. We then use the recovered knowledge to construct models of the world, future scenarios, and possibilities of what could be.

If we view memory in terms of information retrieval, it also makes clear how we might improve our memories. If we want to learn something, we must use it, because use is what our memory system cares about.

I likewise advise my students to be active about their learning. Don’t just passively read over the notes from lecture. It will seem like you understand what’s there, but it’s only an illusion of clarity. Rather, try to explain the concepts to someone else. Generate questions about the material to quiz your own knowledge, or, indeed, generate arguments or problems or come to new ways of integrating and thinking about the material.

By using control to assign ourselves these tasks and shape the use of memory, the brain has an opportunity not just to lay down a memory but to discover the connection of that information to other knowledge we have and to the tasks we are doing. In essence, we don’t use our memories to perform tasks; rather, our tasks are what we use to remember.

CHAPTER 9

Cognitive Control over Lifespan

If you have had any contact with the popular press on parenting and executive function, then you have probably gotten the message: kids are little frontal lobe patients with varying degrees of dysexecutive syndrome, and this is a problem that needs to be identified and corrected before they turn into utter derelicts. Newspaper and magazine articles warn about the correlations between executive function and all manner of life outcomes. Schools promote info sessions about assessing and improving your child’s executive abilities. Popular websites publish lists of vague executive function-y terms like “cognitive flexibility” and “inhibitory control” and caution parents to be on the lookout for signs that their kids might be delayed in developing proper cognitive control function.

What are these telltale signs? There’s lots of information out there, and it is confusing to say the least. One popular site for parents from a highly reputable institution warns that children with executive delays have trouble listening or exhibit stubbornness about getting their own way. “Oh my!” thinks the conscientious parent, “stubbornness and temper tantrums? That is an exact description of my child!”

We should probably pump the brakes a bit here, however. Stubborn and temperamental with trouble listening is also a description of just about every child who has ever lived. If we took a survey of the most successful people in the world today, I doubt a single one of them didn’t have some glorious meltdowns as a small child and did some pretty dumb things as an older one. I’d also wager a dollar that they struggled to finish an assignment or two, or didn’t study well for the occasional test. Hey, I’ll admit to having periodically misbehaved as a child. Did you? Ask your parents.

There are a number of problems with the idea that children’s misbehavior is evidence that they are dysexecutive patients in need of rehabilitation. First, these behaviors don’t, of their own accord, reflect something unusual or worrisome in most children. To be clear, there are real mental health issues that arise in childhood, like attention-deficit disorder or autism, and it is imperative to identify and intervene to help children affected by those illnesses early on. However, not every child who misbehaves in class has a mental health problem. So, outside of trained professionals, the rest of us have to be very careful about looking for signs of underlying problems in an individual child’s behavior, particularly when we have no idea about the frequency or variability of these behaviors in the population at large. Failing to do so places us at risk of diagnosing childhood itself.

Further, it can be problematic to treat all children’s dysexecutive behavior as a “deficit,” as a problem that needs to be corrected. For most kids, dysexecutive behavior is entirely normal. Indeed, it is an essential part of the process of growing a human brain. Though it is important to provide children with an environment that promotes healthy brain and cognitive development, when and how to intervene with a child is subtly different from trying to treat a deficit in a patient. For instance, it might be entirely appropriate to help a patient’s executive issues by structuring their environment to remove any pressures on their control system. However, there might be instances when this is not a good idea for a child, whose brain is using experiences—both good and bad—to learn how to control themselves.

Further, unlike the deficits experienced by many patients with executive dysfunction, a child’s executive function is not an endpoint; rather, it is a snapshot of a dynamic and developing functional system. No one is surprised that a child left to their own devices eats too many cookies or fails to remember their hat and gloves when they leave the house or refuses to share during a playdate. Rather, we are really concerned with assessing where a child’s cognitive control is headed in the future, when they do need to live independently. That kind of prediction is much harder, particularly as different children progress at different rates. The problem is even more complicated if one considers the point that cognitive control is not one mental faculty but, rather, emerges from a highly interactive and complex system for connecting thought and action. So, in sum, it’s important to try to understand childhood cognitive control on its own terms, as a dynamic, developmental process.

What about the other end of our life timeline? Though cognitive control does stabilize to some degree, it does not stop changing when we reach adulthood, either. Rather, our cognitive control ability continues to change, peaking in our 20s and then mostly diminishing as we age beyond 30 years old. By our 60s and 70s, if not earlier, most of us will start to experience these changes in a noticeable way in our lives. Whether we are balancing a checkbook, planning an evening out, carrying on a conversation in a noisy restaurant, or trying to figure out that new smartphone app, everything becomes harder, and life seemingly comes at us faster. So, the elderly often become increasingly limited in their activities, dependent on others to get things done, and may, eventually, lose their independence altogether.

The causes underlying declines in cognitive control as we age contrast with those that drive change in development early in life. Whereas children’s routine control failures are often mislabeled as a “deficit,” this is a more apt description of the changes in aging. The declines in cognitive control as we age reflect changes in the brain and its interaction with the world around us. This is a loss of function, and the natural progression will generally make matters worse, not better. But here too, the news is not all grim. As we will see, there are many aspects of our cognition, including our cognitive control, that are a source of strength and resilience as we age.

In summary, then, any complete understanding of cognitive control must consider it as a dynamic faculty rather than a fixed ability. In this chapter, we will depart from the static view of cognitive control we’ve taken to this point in the book. We will consider how cognitive control changes from our childhood through our old age, and we will conclude with a look at current attempts to intervene and “train the brain” at both ends of the age spectrum.

Developing Cognitive Control

If you have children yourself, or at least have been around our young friends for any extended period, you probably have some idea of where the concept of deficits in children’s cognitive control comes from. Cameron might just keep saying the same word for 20 minutes straight. June might choose to let everyone in the restaurant know that she just used the bathroom. Elroy might wear two different shoes and his shirt backward all day without noticing.

These moments make us grimace or chuckle, but as comedian Ray Romano would point out in his stand-up routine, “Grandpa does that, not so cute anymore. All right? There’s your double standard.” And Ray is exactly right. Neurologists observed early on that adult patients who suffered damage to the frontal lobe acted remarkably like children, and this was a big obstacle in their life.

The ever-dependable 1936 case of Patient A is chock-full of examples of puerile behavior. For example, the following is an account of A getting dressed in the presence of his doctor, his wife, and his mother:

[Patient A] washes his hands. “Why wash my face? The barber will do it for me. He puts a hot towel on it and that’s good enough.” (Soap, it is pointed out, has greater cleansing properties than a hot towel.) A answers, “Balls!”

[…]

A wanders from room to room, making jocular remarks to his mother and sarcastic ones to his wife. He whistles, sings, grins, dances, jigs, and declares: “And I’m some dancer, too. I bet you can’t do that.” (When there is no answer A resumes the fighting pose and pushes L with his fist.) He makes frequent allusions to the [Stock] Exchange and to the [trading] floor. He puts his shirt on, then his trousers, the right leg first, buttons them, but only partially. He puts on his shoes but does not tie the laces. He stands up, slippers in hand, and at this point his mother may appear to take them from his hand.[1]

I think my wife and I have had that exact same morning trying to get our kids ready for school, perhaps only substituting discussions of Minecraft for the New York Stock Exchange. The similarity in these behaviors points to a common system underlying these two groups’ behaviors. In other words, these commonalities motivate the hypothesis that cognitive control is a major and extended locus of developmental change in children. But that is about as far as the analogy goes.

Just as cognitive control is slow to develop, the brain systems important for cognitive control undergo protracted change over the course of early childhood into adolescence.[2] However, this is not to say that the frontal cortex is waiting to develop or is inert until later in childhood. Rather, the process begins in utero, and, indeed, by the time we are born, the brain has already begun differentiating the regions and networks in the prefrontal cortex.

Cell migration is the process by which young cells move around the embryo to position themselves within the developing organism. Cell migration of neurons during the prenatal period is important for the development of the nervous system because it determines how neurons position themselves, group, and connect with one another. Notably, neuronal cell migration is largely from front to back within the frontal lobe, so that cells in rostral PFC are differentiated earlier than those caudally.[3]

In opposition to this, the thalamus has a back-to-front pattern of wiring with the PFC. You may recall that thalamocortical drive is regulated by the striatum and supports working memory gating. Thus, this caudal-to-rostral pattern of corticothalamic wiring may be a source of the asymmetric way that the gating loops support hierarchical cognitive control as discussed in chapter 4.

Further, the opposing patterns of caudal-to-rostral thalamic innervation and rostral-to-caudal cell maturation in frontal cortex may have important organizational consequences. As a structure, the thalamus is effectively the brain’s grand central station. No input from outside the brain makes contact with the brain without first passing through the thalamus. And the thalamus is the major way-stop for sensory information coming from posterior neocortical regions to reach the frontal lobes. As they don’t have this thalamic input initially, those rostral frontal regions mature in the absence of sensory input from the back of the brain.[4] Thus, the early differentiation of rostral prefrontal neurons is primarily shaped by local frontal inputs themselves. This local integrative processing was another feature of the prefrontal architecture for hierarchical control discussed in chapter 4. What is striking is that these features are already present at birth.

Importantly, however, other changes continue to occur throughout the brain after birth and are prolonged in the prefrontal cortex. Though the whole brain increases in size over the early years of life, prefrontal cortex growth is double that of other regions, mirroring the evolutionary expansion of the forebrain in humans.[5]

The prefrontal cortex is also among the last areas to mature. During the course of cortical maturation in all regions of the brain, the thickness of the cortex increases initially and then decreases to a stable level in adulthood. However, the time course of this process differs across brain regions, and progress along this curve can be used to measure the maturity of a given area of cortex. Studies using these measures have consistently found that whereas most areas of basic sensory and motor cortex reach the stable, mature stage by around age three to six, the prefrontal cortex continues its maturation process well into adolescence and until the early 20s.[6] This time course is shown in Figure 9.1. White matter measures show a similar protracted time course for frontal regions relative to other areas of the brain.

One reason for the change in thickness of the cortex during maturation is change in the density of synapses by which neurons talk to one another. Throughout the brain, new synapses form after birth, a process that developmental biologists call synaptogenesis. Synaptogenesis tends to increase initially, greatly expanding the number of synapses, and is then followed by a period of synaptic pruning, during which many synapses are lost. This process of pruning is essential to efficient network processing. Synapses that are not being used are lost, and so neuronal ensembles that fire together develop stronger synaptic connections. This use-dependent change in the cortex is critical, because it is our first clue that changes in the brain are not inexorable but are driven by use. And how the brain is used is driven by a person’s experiences in the world. This process of synaptic change is protracted in the prefrontal cortex, both peaking later and taking longer than in other brain regions.[7]

FIGURE 9.1. Gray matter maturation over the cortical surface. (A) Top views are adapted from Shaw et al. (2008); (B) lateral views are redrawn from Gogtay et al. (2004). The color scale corresponds to the volume of gray matter, with a trend toward thinner gray matter as a marker of greater maturity. Figure adapted from Badre, D., and D’Esposito, M. (2009). Is the rostro-caudal axis of the frontal lobe hierarchical? Nature Reviews Neuroscience 10, 659–669, Box 2. Shaw, P., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. Journal of Neuroscience 28(14), 3586–3594. Copyright 2008 Society for Neuroscience.

Thus, overall, the prefrontal cortex and its associated regions undergo significant structural change through childhood and are continually refined throughout adolescence. People commonly cite the protracted development of prefrontal systems in the brain in their discussion of children’s cognitive control function. However, just as commonly, they draw a conclusion from this fact that is less clearly supported. A common assertion is that longer brain development means a longer period that cognitive control is vulnerable to negative environmental impacts. I’ll call this view the “Death Star model” of cognitive control development. It goes something like this. First, as in the construction of a Death Star, frontal and parietal systems needed for control take a long time to develop. Second, like the Death Star, cognitive control is not “fully operational” until it has undergone its lengthy construction process, at which point it can destroy planets and identify the ink colors of words. The logical conclusion from these two premises, then, is that this lengthy building process makes these brain systems susceptible to disruption by negative influences from the child’s environment, which, like a team of raiding Rebel starships, will stymie the development of cognitive control.

It is true that environmental factors influence the development of cognitive control, as we will discuss. It is also undeniable that extreme or chronic stress or psychological and physical trauma can affect cognitive and emotional development. But outside of these extremes, the idea that the environment primarily has a disruptive role to play in the development of cognitive control is not well supported and requires some skepticism. Let’s see why.

First, consider the premise that cognitive control takes a long time to be a fully operational battle station. It would be exceedingly unlikely for us to have evolved a brain system with such a glaring vulnerability in its setup. The frontoparietal systems of the brain undergo developmental change well into our early 20s. Twenty-plus years is a very long time for a function to “fully” develop or “come online,” particularly in a species that can reproduce well before this point. Moreover, two decades is a long time to avoid anything negative occurring that might disrupt this development. It seems nearly impossible that all but a lucky few of us would pull through intact, which would give “normal” cognitive control a pretty unusual definition.

Indeed, it seems unlikely that any of our ancestors would have managed to develop “normal” cognitive control. The world was far harsher and with more negative life events for our ancestors than we experience today. Thus, if the development of normal cognitive control function required 20 years of living in a supportive, enriched, stress-free, upper-middle-class home with modern parenting sensibilities and Music Together class, very few of our ancestors would have managed it. In evolutionary terms, cognitive control would not have been expressed in our early primate ancestors’ phenotype and so would never have been selected for at all.

The second issue with the Death Star model is that it implicitly assumes that the normal process is inexorable given a normative environment and provides no mechanism for why or how it takes so long, beyond a loose idea that it is “hard.” And as we all know from stereo systems, hard things take a while to set up.

Of course, other hard things the brain does don’t take decades to develop. For example, at the computational level, the process of vision is pretty darn hard.[8] In fact, the inverse problem of translating a 2D image on the retina into a 3D object representation in depth in our mind is an example of the hardest kind of problem. This is because there are an infinite number of 3D object solutions for any given 2D retinal image. Yet, the brain has its heuristic solution to this problem in the first year of life. And how about language? Language is so hard that no artificial system presently comes close to the performance of a four-year-old.

The Death Star model does not explain why cognitive control is harder computationally or needs to take so much longer to wire up than vision or language, beyond an intuition that it is a “higher” cognitive function. The circularity in this line of reasoning should be clear: cognitive control takes many years to set up because it is harder. How do we know it is harder? Because it takes many years to set up!

For these reasons, I don’t place much stock in the Death Star model of cognitive control development. Yes, the brain systems for control continue changing for many years, but the environment plays a more direct, causal role in this progression than just being a source of rotten experiences people have to throw them off a healthy course.

The alternative that I favor is that cognitive control takes years to develop because this is a function that requires optimization and customization. I hope to have convinced you by now that cognitive control broadly serves the function of bridging thought and action. It maps general, abstract, and hypothetical goals to specific implementations in the world. The world is unpredictable enough that the specific side of that equation is hard to program genetically. The kinds of tools we use to accomplish tasks today, like computers, smartphones, and combustion engines, were not on the landscape when our ancestors were flint knapping their way through dinner. Thus, our control systems adapt and are customized to the world we are in, and that type of optimizing requires data. The data in this case come from living and acting in the world. The control system also needs the rest of the brain to be stable enough to provide meaningful data for the control system to learn about the system state it is controlling. Thus, it necessarily matures after other systems have stabilized. From this view, then, the time course of cognitive control development is protracted not in spite of environmental variability but, rather, because of it. It will develop effectively given the data it gets from experience. Control problems can arise, then, to the degree that this adaptation settles on solutions that poorly match the world in which we find ourselves later in life.

Does this hypothesis fit with the evidence on the development of cognitive control? First, evidence of rudimentary kinds of control and inhibition starts to emerge right away in infancy, during the first year of life. For example, avoidance of the “A-not-B” error in infants has been used as a measure of mental flexibility since the classic work of developmental psychologist Jean Piaget in the early twentieth century.[9] In this setup, a child is shown an object it wants being placed under a box. This box is named “Box A.” After the object is hidden, the infant is then allowed to remove the toy from Box A and play with it a bit. This process is repeated a few times, with the toy always hidden under Box A. Then, on the critical trial, the toy is hidden under a second box, called Box B, in full view of the infant. Where will the infant look? If the child is less than 12 months old, they are likely to look under Box A again, even though they recently saw the toy hidden under Box B. This is called the A-not-B error.

One interpretation of the A-not-B error is that it reflects a failure of cognitive control.[10] During all those episodes searching Box A and then getting the toy, the infant gains a tendency to look under this box and is unable to rapidly switch to searching Box B even when they view the object hidden under the new box. They are unable to connect what they know about the location of the object to how they act.

There is evidence that providing some degree of environmental support by changing the context only during the critical B trial, such as by having the child stand, can help reduce the A-not-B error.[11] This seems sensible, as this change would reduce the overlap of the Box B situation with the previous A situations and so would make shifting easier. Cognitive control is needed most when the world around us doesn’t explicitly change, but we have to make a shift of our own internal conception of the world. Yet, by 12 months, children no longer exhibit the A-not-B error, even without context support. So, very early in life, they gain the ability to respond rapidly to changes in the world that oppose previously reinforced tendencies and require a shift of internal context.

The period ranging from preschool, at ages 3 to 4, all the way to early adolescence, around ages 10 to 12, sees the strongest gains in most laboratory measures of cognitive control.[12] Cognitive control continues to improve after this age range, but the change decelerates in adolescence.[13] Though still crucial, particularly for emotional control, the adolescent period can be characterized as one of refinement relative to earlier childhood.

So, the middle-childhood period, between ages 4 and 10, has been a major focus of research into cognitive control development. Though there has been some evidence that certain functions, like basic inhibition, develop more rapidly than others, in general, there is a nonspecific and rapid improvement in cognitive control across all its facets in this age range. And this improvement tends to be correlated with a general increased capacity to handle more complex situations that take account of more and more dimensions about the world to guide children’s actions.

As a case example of this progression, let’s consider rule following and hierarchical control. In general, rule following improves throughout development. “Hold on just a minute!” says the parent of a teenager reading that last sentence, “I have some counterexamples for you.” When cognitive neuroscientists say “rule following,” they are referring not to obeying household rules but, rather, to the capacity to follow an arbitrary instruction in order to perform a task or an action.

Under this definition, by about age three, children are capable of immediately following simple and entirely arbitrary verbal rules, like “please pick up the apple when I say the word mullet.” Presumably, our three-year-olds have never followed that particular rule before, but as capable rule followers, they can grab the apple when I say “mullet.” They don’t have to know what a mullet is and, happily, probably don’t. And they’ll do it on the first try, without multiple prior trials of reinforcement about what to do. However, as the rules become more complex and hierarchical, then the three-year-olds will fail to follow the instruction.[14]

For example, in a procedure called the dimensional change card sorting task, children are given cards with colored objects on them, like red rabbits and blue cars.[15] They are asked to match these cards with other target cards based on either the color or shape on the card. The trick is that the target cards create a conflict between the shape and the color being matched. For instance, the target cards in our example would be a blue rabbit and a red car. So, if sorting by color, the child has to match the red rabbit to the red car.

Despite this confusing state of affairs, three-year-olds can sort the card according to the first arbitrary rule they are given. Then, after they have sorted according to either shape or color for a while, the experimenter tells them the rule has changed, and they must now sort by the other dimension, either color or shape. At this point, three-year-olds will start to make perseveration errors, sorting according to the first dimension and not shifting to the new dimension. Only a year or two later, four- and five-year-olds will perform better at this task, being less likely to perseverate when the rule changes.

So what is happening here? It is possible that young children fail to hold on to the new sorting rule in memory or just fail to implement it in some way.[16] However, another possibility is that younger kids struggle with hierarchical control, as we defined it in chapter 4. When hearing the new rule, a younger child might fail to mentally impose a new context that separates the task they are doing now–color sorting—from the task they were doing a few moments ago, shape sorting. The experimental setup has remained the same, so this shift of context must be entirely internal, in the child’s own head. Thus, having failed to separate the contexts, the two tasks compete directly, and so errors are more likely.

A recent study by Fred Verbruggen and colleagues at University of Ghent provides some evidence for this hierarchical control account, using a different experimental task.[17] In this study, children of different ages were instructed with simple rules that told them to press a left or right button on a keypad in response to a particular cartoon character, as shown in Figure 9.2. They were told this was called the “Go task.” Importantly, after receiving the Go task instructions, but before they actually performed the Go task, the children were given a chance to see all the cartoon characters. After viewing a character, they pressed one of the buttons, for instance always the right one, to see the next character. This task was called the “Next” task.

It might not be obvious on first look, but the experimenters performed an ingenious sleight of hand here. The deceptively simple Next task sets up a bit of a conundrum for the control system. When the children do the Next task, they always press the same key to each cartoon character. However, in the instruction phase that came before, they were told that in the Go task half of those cartoon characters map to the other key. This means that about half the time during the Next task they will press the right button in response to a character they had been instructed took a left response in the later Go task.

As schematized in Figure 9.2, the impact of this overlap will depend on how the child represents this task. If the child is able to keep the Next and the Go task straight in their head by imposing different Next and Go episodes, then there is no overlap and so, little interference. But, if they fail to do so, this overlap of conflicting responses will cause interference, evident in slowed response time during the Next task for these incompatible responses only. Indeed, this latter case is what happened. Children showed greater slowing for the incompatible than compatible responses during the Next task, and the interference was greatest for the younger children, starting at four years old.

FIGURE 9.2. Schematics depicting the design and logic from Verbruggen et al. (A) In the instruction phase (left), children and adults were told that during the Go task, certain characters took a left or right response. In the Next task (middle), they viewed the characters pressing a right response to each. Then, in the Go task (right), they categorized the characters left or right. Adapted from Verbruggen et al. (2018), Figure 1A. (B) Without the imposition of a task context, there is an overlap of response sets for the Go and Next tasks, setting up conflict. (C) By imposing an episodic task context, the overlapping sets can be separated and interference reduced.

In my view, the implications of these observations are fundamental. First, remember that the children have not performed the Go task yet when they perform the Next task. They heard only the rule. Thus, this response conflict is possible only if they have successfully prepared the rule and held it in memory in preparation for the Go task. If memory or preparation were an issue, there should have been less interference in the children, not more. So, though preparation is surely sometimes the issue, in this particular case it is not the explanation.

However, this observation is consistent with a hierarchical control account. Assuming one has prepared the rules, the only way to avoid interference is to impose two different task contexts on this situation. Though the outside world has not changed much, in our mind we need to separate the episode of the “Next task” from the later episode of the “Go task” and their respective response sets. If we are successful at this separation, then interference will be reduced. And this is what is observed in the older children and adolescents. The younger children couldn’t keep the context of the Next task separate from the Go task, and so they were more susceptible to interference. This was a failure of hierarchical control.

An increased capacity for hierarchical control may be a key to understanding the development of cognitive control through this critical middle-childhood period. When you move from simple rules that map mullets to apples, to a dimensional card sort, you add hierarchical levels, which puts pressure on the control system. In chapter 1, we observed the basic principle that the deeper our hierarchy goes, the more we have to monitor and test about the world. Could these added demands specific to hierarchical control be a part of developmental change?

There is at least some evidence for this. Developmental cognitive neuroscientist Dima Amso and I tested hierarchical rule following in children from late childhood to early adolescence while controlling for other aspects of difficulty.[18] We found that deeper rule trees were differentially harder for younger children relative to other kinds of difficult rules. Every time the rule added another level of context, and the tree got deeper, younger children lost ground to older children and adolescents.

Thus, hierarchical control shows developmental change over this critical early- to middle-childhood period. It is important to note that we should not draw the Death Star conclusion from these studies. Hierarchical control does not “come online” as children get older. Rather, there is a process of developmental change that drives the increased capacities we see the child exhibit. In the next section, we will consider the mechanisms by which this developmental change occurs.

The Sources of Developmental Change

What drives this developmental change in cognitive control? This is a fundamental question, because its answer might tell us why individuals differ in their cognitive control ability and, indeed, how we might intervene to ensure a healthy process of brain and cognitive control development. As you might expect, given its importance and complexity, this is also a contentious problem in the scientific literature.

Cognitive and brain function is determined by both genetic and environmental factors, and most important, by their interaction. Environmental factors include the biological environment, such as the hormonal and molecular exposures going all the way back to the uterus, as well as the impact of information processed through the senses. The experiences we have will affect the development of most cognitive functions, and cognitive control is no exception.

To understand the influence of the environment and genetics on cognitive control, we first need to discuss how scientists measure differences in cognitive control capacity among people. I probably don’t need to tell you that people differ in their cognitive control capacity. Speaking for myself, I am a stereotypical absentminded professor. For example, I went on an overseas trip for work recently and lost every international power adapter we own; that’s more than one, and on more than one occasion. In fact, I lost one of them in an airport shortly after texting my wife in frustration about how I’d lost another at the hotel. This is hard to do.

So, people differ in their cognitive control. Scientists seeking to measure these differences, however, face a challenging problem. We want to know how various abstract psychological capacities, like inhibition, differ among people. However, we have no way to directly measure them. We can test tasks, like the stop-signal test, that are designed to tap into those capacities. But no task we do in the lab is pure. When people perform a given task, there are likely multiple cognitive and brain systems involved that interact in complex ways to produce the behavior we observe.

For example, the stop-signal task measures inhibition, but it also involves visual and auditory sensation, spatial attention, motor preparation, language, memory, and so forth. We run controls to address these contributions, but even these controls are not pure, and we have to make assumptions about how isolated inhibition is from all the other factors listed.

To deal with this problem, scientists assume that though no task is pure, multiple tasks will be impure in different ways. So, rather than just using the stop-signal task, we will test multiple tasks that all share a hypothetical component of inhibition. We can then ask how performance is similar across those tasks. For example, someone who is particularly good at inhibition will tend to be good at any task that includes that component relative to tasks that include other components. Of course, a major limitation of this procedure is that we assume we know which tasks involve inhibition or whatever process we are interested in measuring, and that is no simple prospect. Nonetheless, this approach has yielded some general, but consistent, patterns in terms of the differences in human cognitive control function.

Akira Miyake and Naomi Friedman at the University of Colorado conducted a landmark study of individual differences in cognitive control.[19] They defined a hypothetical distinction among three constructs of cognitive control that they termed inhibition, updating, and working memory. Roughly, their conception of “inhibition” qualitatively maps onto what we called stopping inhibition in chapter 6, whereas their “updating” and “working memory” approximate the flexibility and stability dimensions of working memory gating as we’ve framed them in this book. Each of their constructs was tested by multiple tests. So, for example, inhibition was probed by the stop-signal test, Stroop, and a third called the go-nogo test.

The results were both powerful and paradoxical. First, people’s performance could be partially explained in terms of the distinct constructs like inhibition or updating. In other words, people’s performance on a given test of inhibition would be more related to their performance on other tests of inhibition than to a test of working memory, for example. Thus, as we would expect, different facets of cognitive control determined performance in a different ways.

Importantly, however, there was also a general component that predicted performance across all tasks regardless of the specific control function the task tapped. Thus, if a person was good at one of the tasks of cognitive control, they could be expected to also be good at all other tests of cognitive control to some degree.

Miyake and Friedman referred to this paradoxical set of findings as “the unity and diversity of executive function.” In other words, control functions are not fully dissociable units that operate as separate organs, like the heart and liver. Rather, there are likely some general aspects of brain function that will affect all cognitive control performance, and there are systems or factors that contribute to performance on specific kinds of control.

With this complexity in mind, we can ask how genetics and environmental factors influence cognitive control development. Studies conducted on twins offer the strongest investigation of the impact of genetic and environmental factors on the development of both common and specific cognitive control constructs.

Twin studies include both monozygotic (identical) twins, who share 100% of their genes, and dizygotic (fraternal) twins, who share half their genes. Comparisons across these pairs are used to estimate three contributors to performance. First is the influence of genes, estimated from the similarities that monozygotic twins share with each other above those observed in dizygotic twins. Second is the influence of the environment that the twins share; this is the similarity across twin pairs regardless of their genetic similarity. Finally, there is the influence of the unshared environment, which is estimated based on any differences between monozygotic twins that share an environment.

The astute reader might note that the interaction of the shared or unshared environment with genetics is missing from this equation. It is very difficult to estimate this interaction without large samples of twins both raised together or apart, and even if we had such a sample, their assignment to these groups would not be truly random. Given what we know about genetic and epigenetic effects, this interaction component could be a quite substantial contributor to individual differences, and so its absence is a major limitation for any conclusion one draws from human behavioral genetic studies. Nonetheless, this caveat notwithstanding, the evidence we have from twin studies is important to consider.

Twin studies of cognitive control have consistently found that shared genes explain nearly all the individual differences in the common cognitive control component, as in the component related to performance across all the tests of cognitive control.[20] Across multiple studies of children and adolescents and with different mixes of socioeconomic status, education, race, and other demographics, this component has been found to be around 99% heritable. There is little to no influence of either the shared or unique environment. Further, unlike other putative traits such as general intelligence that grow in heritability toward adulthood, the common cognitive control factor appears to be highly and equivalently heritable in children, adolescents, and adults.

Importantly, it would be a mistake to interpret this high heritability as meaning that the environment plays no role in the development of cognitive control. First, this high heritability applies only to the common cognitive control component. As we will discuss in a moment, there is lots of room for environmental influence on the arguably more important specific control components. Second, though diverse samples have been used in some studies, most individuals included in these studies still fall within a restricted range of environmental effects. Genetic studies of IQ have consistently found that heritability increases with socioeconomic status,[21] and so a similar phenomenon may influence this result on cognitive control. Third, as already noted earlier, the critical interaction term between genes and environment is not estimated in this analysis. Finally, it is not known whether or how unusual environmental factors, such as extreme neglect, abuse, malnutrition, and so forth, would affect the common control component. Nonetheless, these observations do indicate that a portion of our performance common to tests of cognitive control will be a stable individual difference based on our genes.

It is not fully clear what this common cognitive control ability corresponds to biologically. However, a recent genome-wide analysis of 427,037 people from the UK Biobank identified 299 loci associated with an estimate of the general cognitive control construct.[22] In broad terms, these loci were related to biological features of the brain having to do with the formation of fast synaptic pathways and the prevalence of the neurotransmitter GABA. It remains to be understood how or why features like fast neural kinetics or GABA would be broadly important for cognitive control. Nonetheless, these factors are too general to change across tasks requiring, say inhibition or shifting, and would be hard to learn. Thus, they are unlikely to explain the diversity side of the paradox of individual differences.

Whereas the common cognitive control component might be highly heritable, this is decidedly not the case for the more specific cognitive control constructs or performance on individual tasks that confront us in everyday life. For example, in one study of twins aged 7 to 12, the unshared environment was the major determinant of performance on the stop-signal task, followed by the shared environment.[23] Genetics was last in order of contribution. Reduced heritability for these specific tasks is partly due to the task purity issue that we already discussed. However, imprecise tasks aren’t the whole story. Heritability is also lower for the individual control constructs, like updating or inhibition, that derive from multiple tasks. In the previously mentioned study, the unshared environment between the twins was the primary contributor to inhibition, updating, and working memory.

This environmental influence makes sense for individual control functions, given the importance of learning for setting up our control system. Consider, as an example, working memory gating. In chapter 3, we discussed how getting the gating policy right for a given task is critical for performing well on that task.[24] We need to learn not only the rules of the game but also how to implement these rules in terms of the input and output relationships through working memory. As hierarchies get more complex, gating is particularly critical for managing multiple levels of goals, and the correct gating policies are learned through experience.

In our collaborative work, Dima Amso and I, along with postdoc Kerstin Unger, observed that 7-year-olds appeared more likely than 10- and 12-year-olds to choose the wrong gating policy during the context-first/context-last task discussed in chapter 3.[25] This failure to choose the right gating policy accounted for a portion of their poorer performance relative to that of the older children. Thus, children may not always be unable to engage in control. Rather, they simply haven’t found the right way to break the task down into its component parts, to gate the ins and outs of working memory, in order to perform the task effectively.

So, during that extended and crucial middle-childhood period, children might be learning what and when to gate. They are busily laying down increasingly abstract gating policies that they can apply to more situations and that allow them to accommodate increasingly complex tasks. They are learning how to engage in internal control. And, of course, they are doing so while constrained by the development of perceptual, conceptual, language, motor, and other systems at the same time.

This view of cognitive control development, then, places a special emphasis on learning and experience, and in particular on the diversity of experience we have during childhood. To build useful and abstract gating policies that apply in lots of situations later in life, we need to try to control ourselves in lots of different settings.

Computer models of cognitive control in neural networks exhibit this basic property. Take as an example the corticostriatal model of gating discussed in chapters 3 and 4. Many cycles of training allow this model to learn which inputs to gate into working memory and when to gate them out based on dopamine prediction errors.[26] Likewise, presenting these models with multiple different tasks allows them to generalize and create abstract contextual representations that are reusable components of tasks rather than useful only in a specific task.[27] Thus, these models give us an existence proof for our hypothesis. They show us that setting up a gating system for cognitive control requires learning and diverse experience to get its gating right.

In real-world terms, this idea fits with data showing that environmental enrichment is a key to developing a cognitive control system that is effective in a wide range of novel circumstances. Enrichment is defined in terms of the diverse experiences and learning environment of a child. Enrichment has long been associated with positive learning outcomes, and this includes cognitive control.[28] Thus, one account of these observations is that enriched environments allow children to develop abstract gating policies that are widely applicable in novel situations. This is helpful later in life, when most children find their adult world to be different from that of their childhood. They have a big library of gating policies on which to piece together solutions to problems as their goals demand.

Another appeal of this diverse learning account is that it provides an explanation for the lengthy course of cognitive control development. It is necessary for gathering as much data as possible from experience, which optimizes our control system for the world we live in. Essentially, the brain assumes that the first decade and a half of life is a sample of how life will go for the remaining 65 years. As such, it optimizes control on this basis. This also means that cognitive control will be only as effective as the validity of this assumption, on how good a model it builds. Like any statistical model, if it gets lots and lots of data—a useful sample of the kinds of demands it will encounter later in life—it can probably produce a better outcome.

This emphasis on learning and experience using control also warns against the trend toward increasingly interventionist parenting. The “helicopter parenting” of the 2000s has evolved into the “lawnmower parenting” of today that seeks to remove every obstacle that stands in the way of a child, whether at school or home. The extreme of this parenting style removes the opportunities for children to be autonomous and to make their own choices that lead to success or failures.

There are many understandable reasons for this trend. Concerns over safety along with a compassion for our children and a desire for them to feel confident and successful are important driving pressures. There are also social pressures. Parents are keenly aware of the need for their children to succeed in an increasingly competitive academic environment. Leaving it up to the kids to remember to pack for school or do their homework is out of the question. And for many parents, even if they wanted to encourage greater independence in their children, it would be difficult to do so in today’s world. Even if a child was allowed to walk to the park on their own to play, who would they play with?

Of course, children are capable of considerable autonomy. Indeed, this way of growing up would have been unfamiliar to children of most prior generations. Children long had the ability to engage in free and unstructured play. They were given free rein to wander their local environment, through parks, streets, and woods. They were allowed to form in mixed-age groups and come up with their own goals, games, rules, and solutions to their own problems. Many of these were no doubt bad or even terrible ideas that led to some failures. But if it is safe, failure is also a tremendous learning tool, and particularly for developing cognitive control.

For example, the autonomy to succeed and fail is a learning principle already known by good coaches of children’s soccer teams. As novices, children do not do a good job of positioning and spacing themselves effectively on the field. They wait too long to pass, if they pass at all. The temptation as a parent in these settings is to tell your child where to run, when to kick, whom to kick to, and so forth. Unlike exasperated parents, however, good coaches wait until the child makes the decision—or fails to do so—then they praise or correct them, accordingly. They point out what the child could have done in that situation. But it’s important that they gave the child the chance to make the decision first. Coaches do this because if they tell the child what to do constantly, the child never learns to interpret the context to take the right action on their own. They never lay down the right control policies that can interpret the dynamic system of the game and select good actions. They learn only one control policy: listen to coach.

Life isn’t a soccer field. So, what should we do as parents to create a safe but effective learning environment for our children? One factor that appears promising is carving out time between piano lessons and sports practice and homework for children to have unstructured activities. Indeed, initial studies of unstructured or semistructured learning have found evidence of its benefit for developing cognitive control.[29] In these studies, unstructured play refers to times when children can determine their own goals and tasks, structure their own plans and activities, and figure out ways to solve their own problems. Thus, having these opportunities may be particularly important for learning self-directed kinds of cognitive control, as opposed to following direct instructions given by someone else.

Thus, if children have a range of opportunities to confront, struggle, fail, and solve new problems, particularly doing so on their own, their brain will have the opportunity to lay down abstract and effective control policies, as well. Caregivers should resist the temptation to “just do it for them” and look for opportunities for children to be autonomous and to succeed, and indeed, to safely fail. Having these experiences can optimize a control system ready to adapt itself to a range of new circumstances later in life. This is all the more important because, as we will see in the next section, when we age, our control system becomes a prime source not only of challenges but also of support.

Cognitive Control as We Age

Most people are aware that cognition declines as we get older. In a 2009 Pew Research Center poll, respondents of all ages were asked what change in life marked the onset of “old age.”[30] Of the top five responses, two were a specific age threshold—like 65 or 85—but the other three were all arguably related to cognition: a loss of independent living, loss of the ability to drive, and memory failure. Other markers not related to cognition, like having grandchildren or (wince) gray hair, appeared much farther down the list.

Yet, aging and cognition are a mixed bag. While people commonly cite memory declines as we age, memory is not necessarily the only or even the predominating problem. Many aspects of cognition broadly decline as we age. Tests of perceptual discrimination, reasoning and decision making, spatial visualization, and even simple motor speed all show steady declines as we age. However, as we will see, other functions improve as we age.

Figure 9.3 redraws a classic figure from the work of Tim Salthouse and colleagues at the University of Virginia.[31] This figure plots scores on four broad psychological factors from different people aged 18 to 84, binned by decade. The first point to notice is the general decline across three of the four factors. Performance on memory, fluid intelligence, and psychomotor speed all start to decline right around age 30. Further, the decline is mostly linear, but it is not an entirely straight line; the loss also accelerates as people get older. These trends match the common impression that aging affects cognition broadly and negatively, though it might surprise some that the decline starts as early in life as it does.

Importantly, however, it is also evident from this figure that not all cognitive faculties exhibit this decline. Vocabulary scores actually increase over time, decelerating till becoming stable by about age 50. Vocabulary is a marker of so-called crystallized intelligence, which is a contrast to fluid intelligence. Scientists use the term fluid intelligence to mean our raw problem-solving ability and our ability to behave and think nimbly in novel situations. It is a concept closely tied to cognitive control. Crystallized intelligence, by contrast, centers on our use of our own knowledge stores and skills. Tests, like vocabulary, that draw on crystallized intelligence tend to be stable or improve with age.

FIGURE 9.3. Performance across lifespan of four major components of cognitive performance (gF = fluid intelligence). Adapted from Salthouse, Atkinson, and Berish (2003), Figure 4.

So, the good news is that not all cognitive functions decline with age, and this goes beyond the fact that your Aunt Zelda knows the definition of the word “ginchiest.” For example, older adults are adept at emotional regulation.[32] They tend to have a positivity bias in memory and attention, focusing on objects in the world or experiences that lead to positive feelings. Further, there is evidence that older adults are driven by intrinsic motivational cues, like accountability to others, more so than younger adults, who tend to be motivated by external reward. This even applies to cognitive effort, as discussed in chapter 7. Some evidence suggests that older adults tend to be highly judicious, even miserly, in allocating their precious cognitive resources, but they are very good about allocating effort to those goals that motivate them.[33] Yes, Aunt Zelda may occasionally forget her keys, but when she says she’ll be at your school to help you pick up the kids, she will be highly motivated to do so and will spend her mental energy making sure she is there. In fact, she might be more reliable in that regard than your younger cousin Cameron, who crafts artisanal soaps. Thus, there are many aspects of our cognition that can be a source of considerable joy late in life, and these are likely among the keys to aging well.

This being said, however, cognitive control function is primarily on the negative side of this ledger.[34] Across nearly all tests of cognitive control, we perform worse as we get older. There has been considerable debate over the years about whether a particular aspect of control gets worse, like inhibition or working memory. And though there is some evidence that working memory may be somewhat more vulnerable than other factors, there is evidence that all aspects of cognitive control will show some age-related decline. This is particularly the case in novel settings, like traveling; or with new open-ended problems; or with tasks with which we have little experience or practice, like figuring out that new smart TV.

This broad decline in cognitive control function is likely one of the factors that make it progressively difficult for many older adults to independently perform the tasks of everyday living, from making dinner to getting dressed to paying bills. Self-care also becomes more demanding as we age. Older adults typically have more medicines to manage,[35] more self-monitoring to perform, and more special treatment regimens to follow—all while managing a body that is becoming slower, less mobile, and less resilient. Failures in these tasks can lead to further health problems, complications, and even hospitalization and death. So, the stakes for cognitive control are high as we age.

It is clear that changes in cognitive control play a role in older adults’ loss of independence in the activities of daily life. Figure 9.4 redraws data from a large study of more than 4000 people aged 3 to 93 performing a set of everyday tasks, like making sandwiches, in the presence of expert raters.[36] These raters assessed the task performance for motor and process competency. Process competency resembles our concept of cognitive control, as it concerns the parts of the task related to attention, initiation, sequencing, organization, adjustment, and accommodation. This measure is admittedly crude, but its advantage is that it is applicable across a large age range.

FIGURE 9.4. The average process score for performance of independent activities of daily living across lifespan. Results adapted from Hayase et al. (2004), Figure 2.

The process ratings across ages make clear how our everyday task control changes as we age. Ratings ramp in early life to peak in our 20s. Then, after around age 30, there is a slow and linear decline that ends at a level approximately equivalent to that seen in the early adolescent years. And, of course, that’s an average; there are some people who do better and, unfortunately, many who do much worse.

This study did not include direct measures of cognitive control, but similar studies have done so. These found that declining performance on tests of cognitive control due to age correlates with declines in independent activities.[37] Thus, a loss of cognitive control as we age will contribute to a loss of task performance and declining independence, even among those who are otherwise healthy and have the financial means to live alone. As our aging population grows—by some estimates 1 in 4 Americans will be over 65 in the US alone by 2060—the burden of care needed for our older population will be a major societal concern.[38] Understanding how to support cognitive control function so people can live rich, full lives as they get older will be important.

Sources of Age-Related Changes in Cognitive Control

What causes these changes in cognitive control as we age? At least part of the answer relates to changes in the brain itself. It is well known that the brain loses volume over our lifespan. This includes both thinning of the cortex and changes in white matter. Importantly, however, volume loss is not uniform in the brain. Notably, the lateral and dorsal medial frontal cortex are among those areas that show the steepest volume loss.[39]

To illustrate this change, Figure 9.5 redraws data from one of the most ambitious studies of brain aging I know.[40] Brain volume in multiple brain regions was measured in a large sample of people across a wide age range. Further, experimenters tested people longitudinally, taking measurements from each person in different years to see how the brain of a single person changes over time. The trends plotted in Figure 9.5 represent the change over 60 years reconstructed from these short longitudinal measurements. The pattern of these results should by now be familiar. In the lateral frontal cortex volume change, plotted on the left, there are two inflection points in the trend line, where the rate of brain atrophy appears to increase. The first inflection is at around 30 years old, and the second is a sharp decline starting at around 60. There is not a similar degree of change in the occipital cortex, plotted on the right of Figure 9.5.

Though we can’t relate the figures formally, the relationship between the plot of cognitive decline from Salthouse shown in Figure 9.3 and the loss of independence shown in Figure 9.4 is tantalizing. The data show a gradual decline from age 30 that accelerates and becomes severe after 65 or so. Observations like this have motivated a major hypothesis in the aging literature, sometimes called the “frontal hypothesis.” This hypothesis states that declines in cognitive control stem from corresponding progressive changes in frontal cortex integrity.

FIGURE 9.5. Volume changes over lifespan in the lateral frontal cortex (left) and occipital cortex (right). Dashed line plots men; solid line plots women. Adapted from Pfefferbaum et al. (2013), Figure 2B.

Of course, we have seen that frontal cortex is only one part of the brain’s system for cognitive control. Another major contributor is the basal ganglia, particularly the striatum that interacts with the frontal networks to enact working memory gating operations. The striatum, including caudate and putamen, also decreases in volume with age.[41] Further, availability of the neurotransmitter dopamine shows a reduction as a function of healthy aging.[42] These changes in dopamine mean that the balance of Go versus NoGo influence on gating shifts as we age. This shift, combined with the changes in the cortex, could plausibly result in the reliable and pronounced working memory updating problems observed in old age.[43] To the degree that this picture is accurate, then, it is easy to see how complex and hierarchically structured tasks required for everyday life—which place more demands on working memory gating—would become particularly challenging for older adults.

The frontal hypothesis is an important and plausible one, but an important question it leaves open concerns why—or how—these changes in the brain occur with age. There are likely several different contributors, including metabolic, anatomical, and vascular changes associated with the aging process. However, it remains a frontier of research to establish how these general changes throughout the brain account for the differentially greater changes observed in frontal cortex.

Moreover, the causal relationship of changes in the brain to the loss of cognitive control remains to be established. Indeed, rather than a loss of tissue in frontal cortex resulting in a decline in cognitive control, it is also plausible that changes in the efficacy and use of cognitive control as we age might cause changes in control networks in the brain. We have already mentioned some ways that older adults change the way they use control. They tend to be more judicious and reactive and show differences in their drives and motivation. As we saw in chapter 8, a tendency to engage control more slowly can itself lead to higher demands on control systems, which result in control failures. This change in the use of control could, in turn, affect brain structure and function. Indeed, recent studies suggest that both directions of causation might be at play.[44]

A final factor to consider is the degree to which our control system is prepared for the tasks we do as older adults. Again, we can think of our childhood as an attempt to predict what our world will be as adults. However, human society is always changing, and so the world we occupy as we age becomes increasingly remote and different from that formative world of our childhood, and not just in hairstyles and music preferences or even prevailing norms of the day. The basic operators we use to get things done change, too. Consider how much the Internet, smartphones, and, now, machine-learning algorithms have changed what is possible to achieve our goals. Many of the policies useful for operating in the world as it is now will not resemble those that were useful to a child of the mid-twentieth century. This is a hard problem to quantify, but it is logical that part of the change in our control capacity is not just about a change in the brain or a change in control style; rather, it is due to a growing misfit between the world we optimized for as children and the world we occupy. This misfit will likely result in a general overhead for using our control systems in old age, an overhead absent in younger people.

Compensation and Reserve

Regardless of the source of decline in control systems, a widespread observation is that relative to younger people, older adults tend to overrecruit the brain systems for cognitive control when doing a difficult task.[45] Neuroimaging studies of older adult brains consistently show that when older adults engage control systems, they tend to show greater activity in cognitive control networks than younger individuals, not less. What accounts for this paradoxical observation?

One possibility is that older adults actually lean on their cognitive control system to compensate for issues found in other systems. As a result, when motivated to do a task, they will engage control systems more than a younger person, even given its diminished capacity.

Patti Reuter-Lorenz at the University of Michigan and Denise Park at the University of Texas at Dallas have proposed an influential theory of cognitive and brain aging they call the Scaffolding Theory of Aging and Cognition, or STAC.[46] They propose that when confronted with tasks for which cognitive or motor systems may have declined, older adults attempt to compensate by engaging the brain systems needed for cognitive control. For example, if older adults are having a hard time reading because of reductions in basic perceptual processing, they might compensate by allocating more attention to the page or using planning to seek ways to enlarge the print, and so forth. So, control becomes a means of solving the problems of aging as they engage in tasks they want to perform.

The focus on cognitive control as a means of compensation holds a close relationship to the theory of cognitive reserve.[47] Cognitive reserve is a term used to describe the observation that some people’s cognition ages more gracefully than others, and further, these same people are less affected when they suffer insults to the brain, like stroke. The metaphor of cognitive reserve, then, is that these people have a deeper well of some cognitive resource on which they can draw to compensate for life’s slings and arrows. Understandably, then, a major area of research has sought to understand the basis of cognitive reserve.

Individual differences in cognitive control have been among the hypothesized sources of cognitive reserve.[48] From this perspective, cognitive control compensates for any number of problems with the brain and cognitive systems, whether due to brain damage and disease or to healthy aging. As individuals differ in their cognitive control capacity, then, their capacity to compensate will also differ accordingly. There is preliminary support for this hypothesis. Specifically, individual differences in laboratory tests of cognitive control tend to correlate with other estimates of cognitive reserve. However, we should interpret this result with caution. As with most correlations of this kind, the causal direction of this relationship has not been established.

The STAC and cognitive reserve models highlight the dynamic relationship of control and cognitive aging. While reduced cognitive control capacity may be a source of difficulties that older adults experience, it is also a source of compensation for decline. Indeed, if older adults use a diminishing cognitive control capacity to compensate for other diminishing functions, then this might help to explain the accelerating rate of overall loss we observe in plots like those in Figures 9.3 and 9.4. In other words, a steep cognitive decline is buoyed to some degree by control systems, but this means that the slope of that decline is itself determined by the integrity of control. As the crutch on which everything else depends gets shakier and shakier, declines in overall function will become steeper and steeper as we age. When that crutch itself breaks, then there is catastrophic loss of independence.

Given the positive impact that cognitive control has in compensating for other functions, considerable work in clinical and applied science has focused on training cognitive control systems. Training cognitive control is an objective both in cognitive aging and in child development, but the evidence has been mixed. In the last part of this chapter, we will take a close look at the current state of research on training interventions designed to improve cognitive control function.

Game Changing? Cognitive and Brain Training for Cognitive Control

The last decade has seen a growing public enthusiasm for cognitive training. There are several proprietary therapeutic training programs on the market and countless more being researched that claim a wide range of benefits, including improved focus and memory, better math and problem-solving skills, and even some relief from psychiatric problems like addiction or ADHD. Most offer a training program, either in a series of sessions with a trainer or, more recently, via an app downloaded to your phone. Just type “brain training” into your smartphone application browser; there are hundreds of such programs available, some brandishing credentials from famous doctors or scientists, and some claiming to be backed by published scientific research. Regardless of whether these are done with a trainer or on an app, the goal is similar. By engaging a specified program of mental activities, often called brain games, the brain is purportedly changed for the better and cognition improves.

It is not surprising that brain training is as popular as it is. A growing older adult population has intensified demand for ways to preserve brain and cognitive health later in life. On the other end of the age spectrum, parents look for solutions for gaps in their children’s performance, or even for a leg up beyond what can be achieved in the classroom.

Indeed, the marketplace for cognitive training has emerged, and it is big business. The “global cognitive assessment and training in healthcare” market accounted for over $1 billion in 2016 and is forecasted to grow at a compound annual growth rate of 34% from 2017 to 2024.[49] As a point of reference, the over $900 billion pharmaceutical industry is projected to grow by 6% or so by 2021. So, brain training is big money, and it is getting bigger by the year.

I’ll add that it is important not to be overly cynical here. People working on brain training are not just out to make a quick buck. Many scientists and clinicians are involved in developing cognitive training interventions with an entirely noble motivation: real and effective cognitive training could do substantial good in the world, and there are domains of cognitive training where the data support training interventions. Indeed, maybe you have taken advantage of a therapy yourself, and it worked for you. That is the promise these interventions hold, if they are done right.

Cognitive control function has been a major focus of brain training, and this is no coincidence. We have seen the widespread impact of cognitive control on everyday functioning throughout this book. Just imagine the benefits to society of adding a few more years of independence to older adults’ lives or, indeed, helping children mitigate any unfortunate negative effects of their environment. If one treats the correlations between control and other life outcome measures optimistically, improving cognitive control would have widespread benefits on everything from academic advancement to job performance to personal health.

For those interested in improving cognitive control, working memory has been a central target of brain training. This is sensible; gating and maintenance of rules and task contexts in working memory are core mechanisms of cognitive control. Likewise, better performance on complex working memory span tests is closely associated with individual differences in cognitive control.

Thus, if one assumes these correlations are causal, improving working memory can have widespread benefit on cognition. Indeed, many working memory training programs promise just that. As of this writing, one popular and widely offered commercial working memory training intervention claims on its website a wide range of benefits that include, among other things, improvements in math and reading in underperforming students, greater independence in older adults, and improved attention in patients with ADHD. That’s an astounding range of positive effects! If a drug were marketed with a similar profile, no one would hesitate to call it a “wonder drug.”

However, unlike drugs, cognitive interventions are not required to demonstrate their safety and efficacy to the satisfaction of agencies like the US Food and Drug Administration before they can go to market. Thus, it is essential that as consumers we treat claims of cognitive control training efficacy with a healthy dose of skepticism, and our skepticism should be greater the more fantastic the claims. Remember, regarding cognitive control, you are trying to improve a function in weeks or months that nature worked on full-time for two decades to tune up. So, let’s take a closer look at evidence supporting brain training.

The spike in enthusiasm for cognitive control training in the cognitive neuroscience community can probably be traced to a set of landmark studies conducted in the early 2000s by Torkel Klingberg and colleagues in Sweden.[50] This highly innovative, though exploratory, work was conducted on children with ADHD using small samples: seven children in the treatment group and seven controls.

The children were trained to perform four working memory tasks. The training was adaptive in that it would get more difficult as the children performed better on the tasks. For example, in one of the tasks, the children would see a grid of circles. A sequence of circles would light up at different locations, and the children were asked to remember the sequence and respond by indicating which circles lit up in their proper order. As training progressed, the number of circles in the sequence to remember would increase. The control group, by contrast, always did the easy version of the test, with just two circles to remember in sequence.

The paper reported striking benefits from the adaptive training. First, the children in the treatment group showed greater improvement than did the controls in a test of spatial working memory using Corsi blocks. In this test, a board with a set of blocks fixed to it at different locations is shown to the participant. The tester touches the blocks one by one in sequence. The participant then touches the same blocks in the same sequence and is given a score based on the maximum sequence length they can reliably remember. So, as you can see, the Corsi blocks and the circles on the screen basically test the same function: spatial sequence memory.

This training benefit is called near transfer, because training on one test improves performance on another test that is only superficially different. Near transfer is important to show, as it is the minimum level of generalization we expect from any kind of training, but near transfer is not really the goal of cognitive training. No one downloads an app to get better at some contrived spatial working memory test. Rather, most people use cognitive control training because they want to improve their performance generally, when doing tasks in the real world. Thus, what a training procedure needs to demonstrate is far transfer. Far transfer is improvement on tests that are unlike the trained test or in different domains.

The reason the Klingberg study caused a stir is that it reported evidence of both near and far transfer. Relative to the control group, the seven patients in the treatment group also improved their performance on the Raven’s Progressive Matrices test. In this test, the participant has to figure out a pattern from three samples and then predict what the next item in sequence will be. For example, a pattern that shows a box with a dot in the upper left, then a box with a dot in upper right, then a box with a dot in the lower right, might be understood as the dot shifting clockwise in sequence. Thus, a correct response for the next item in sequence would be a box with the dot in the lower left. This is not a working memory test; it is a reasoning test. One’s ability to solve these problems is closely related to fluid intelligence, and so improvements on this test were seen as an exciting demonstration of far transfer.

Additional studies from this group and others reported replications and extensions of the original findings, with evidence of far transfer to other tasks beyond matrix reasoning. Further, the far transfer followed a dose-response profile such that the length of time in training affected outcomes proportionally, and those who responded most to training also exhibited better transfer.[51] And, as one would expect, evidence from brain imaging studies showed changes in the frontal and parietal brain networks correlated with training.[52]

Following these establishing studies, research in this domain flourished. Beyond these behavioral training interventions there were developments in the use of video games as a means of training, as people are more willing to engage for longer with video games than with boring laboratory tasks.[53] One such study in the premier journal Nature reported far transfer effects in older adults who played a driving video game.[54] Others began to focus on combining training with brain stimulation techniques, hoping that stimulation might make the brain more plastic and so enhance the impact of training. The increased research in this area that arose during this period has been a lasting positive.

Importantly, however, there were also a number of limitations in these studies.[55] Beyond small sample sizes, many failed to include enough cognitive tests to really measure the underlying cognitive construct. As noted earlier in this chapter, no cognitive test is pure, and so to fully assess improvement in something like fluid intelligence, it is important to test more than just matrix reasoning. Some studies that did use multiple tests found inconsistent far transfer across those tests, without explanation. Indeed, in some cases, far transfer was observed in the absence of near transfer, which simply should not happen. When inconsistent effects are observed across multiple measurements, scientists worry that the apparent improvement on a couple of the tests was simply due to chance.

Perhaps more worrying than measurement problems is that a surprising number of training studies failed to use adequate control groups. Some did not include a control group at all. Others would contrast a treatment group that received weekly or daily training sessions against a control group that was not seen at all during the training period. The problem with a “no contact” control group is that many factors can drive an improvement in the treatment group compared with this group that have nothing to do with training. For example, perceived benefits might be due to placebo effects. No one believes that doing nothing for a couple weeks will improve their cognition. These factors can affect both groups’ motivation and performance at assessment.

Importantly, the preceding limitations did not apply to every published study. Some, including the initial Klingberg study, reported positive effects of training while including an active control group, but it was still hard to know how reliable these observations were. Several published studies had attempted replication and had failed to find effects, and there were likely many more that failed and so were not published. Further, no study had utilized the kind of preregistered randomized double-blind clinical trial that is the gold standard of reliability for a health intervention.

With these limitations in mind, a series of meta-analyses were conducted on the numerous training studies available in the published literature. These analyses formally analyzed the range of published effects by statistically aggregating a number of small samples into a large sample to attempt to assess whether far transfer effects were being reliably reported in the literature; we encountered this approach when considering the evidence for ego depletion in chapter 7.

In 2013, Melby-Lervåg and Hulme published a meta-analysis of 23 randomized trials that had included a control group, whether it was an active or no-contact control.[56] They found evidence of near transfer effects across studies, but they did not find reliable effects of far transfer. A second meta-analysis published two years later by Schwaighofer and colleagues found the same result.[57]

Three years later, and after some debate and dueling meta-analyses in the literature, Melby-Lervåg and Hulme published a new meta-analysis that included a larger sample of 89 working memory training studies.[58] Despite the larger sample size, this meta-analysis again found no evidence of far transfer across multiple different tests. Further, they reported evidence of publication bias in positive training reports, which meant that groups finding effects of training were more likely to publish than those not finding effects, and this was artificially inflating the apparent reliability of published evidence.

In the years since that 2016 meta-analysis, several more meta-analyses have been conducted, as well as some randomized clinical trials.[59] As of this writing, these assessments have agreed with the Melby-Lervåg and Hulme conclusions and have extended them. All have failed to find evidence of far transfer, regardless of training protocol. Separate analyses have found no effect of training on academic skills,[60] on children with pediatric disorders,[61] or from brain stimulation.[62] One systematic review of this cognitive training literature published in 2018 concluded pessimistically that “the cognitive-training program of research has showed no appreciable benefits, and other more plausible practices to enhance cognitive performance should be pursued.”[63] However, I agree with only the first half of this sentence, as I will describe further.

First, one question you might be wondering is, why has cognitive training not worked out? After all, if you spend time lifting a weight, you build muscle. Then, you can use that bigger muscle for all kinds of other activities beyond lifting that exact weight. If the time athletes spent in the weight room didn’t pay dividends on the field, then they would not pursue the training. Why doesn’t cognitive control work the same way?

There are several answers to this question. First, it might be that cognitive training will work, even using the adaptive training protocols currently in vogue, but we simply haven’t found the exact right way of doing the training yet. Despite years of research, we still know very little about the human mind and brain. And so though training protocols have some theoretical basis, in their specifics they are something of a shot in the dark. There are countless small decisions to be made when we put together a training protocol: How long to train? How frequently? How long should that picture they need to track with their attention be on the screen? Which hand should press the button? Should there be a button to press at all? And so forth. Most of these decisions are made entirely based on intuition and guesswork; there is no formal theory to guide them. There are many options, and we haven’t come close to trying them all yet. Maybe the right combination is out there.

Another possibility, however, is that these specific training protocols will never work, given that they are designed based on fundamentally incorrect assumptions about the human cognitive control system. Once again, there isn’t a CPU, a single cognitive controller in the brain that does it all. As cool as it would be, then, there also isn’t a single muscle you can train to increase cognitive control ability. In fact, as we have discussed, if there is a general cognitive control component, it appears heavily influenced by genetics and so is impenetrable to experience. Thus, it is not a good target for training.

By contrast, the specific side of cognitive control is greatly influenced by the environment. If you train on one task repeatedly, you will improve at that one task and maybe at tasks closely related to it, but there will not be far transfer. This is because the improvements seen on the trained task could be based on any number of small adaptations and optimizations customized only to the tasks you trained. Even things like knowing where on the screen something will appear or the range of possible items you will see can yield little improvements. The brain doesn’t care if you are getting better at a task because you have discovered an abstract policy that will generalize to other settings or have just figured out a clever trick that will work only for that task.

From this perspective, it would actually be quite surprising if we saw broad benefits from narrow training. If anything, our discussion of development and the building of abstract control policies argues for diverse training across lots of different tasks. Of course, diverse training requires a lot of data. And so, at present, it is not fully clear how we would build such a training program into an intervention short of reproducing the first 10 years of our lives. Nonetheless, this problem is likely where cognitive training needs to go if it is ever to help cognitive control more broadly.

Where does this leave us? To start, we should treat any claims of wide-ranging benefits from cognitive training of working memory or cognitive control with reasoned, but considerable, skepticism. Even citing “published research” does not mean there is strong and rigorous evidence that a given training program or app is effective. At a minimum, efficacy should be demonstrated in a properly designed, randomized clinical trial. That trial should be in a large sample of people, and it should include an active treatment control. Ideally, it would be double-blinded, such that both those doing the training and the experimenters doing the testing are unaware of which treatment condition a person received. Finally, a trial should have preregistered its specific design, analysis approach, and predictions before the data are collected, and that registration should be in a publicly available system that requires it to report its results regardless of the outcome. One example of such a system is the database at clinicaltrials.gov, the website of the US National Library of Medicine of the National Institutes of Health.

Further, this is the minimum burden of evidence. The sufficiency of the measurement and assessment of far transfer, the rigor of the analysis, the characterization of dose-response effects, the sustainability of positive effects, and several other factors are also crucial. Admittedly, many of these dimensions of a study are quite technical and thus difficult to assess, even for experts. Yet, they fundamentally affect the strength of evidence. Just because a study is published does not mean it has met these standards. Indeed, as of this writing, I know of no cognitive control training procedure that has met even the minimum burden of proof from a preregistered trial, to say nothing of these more technical points. But this does not mean that none ever will. The hard problems are often the important ones, and cognitive training is no exception.

It follows that evaluating claims is important not just because we could waste our time and money on ineffective training games, or even because there is a concern that these interventions will do unintended harm. Rather, it is important because a widespread reckoning of overinflated or premature claims of cognitive training might take good science down with it. It could diminish public faith and support for careful research into interventions.

I am optimistic that as we gain better understanding of the basic science of cognitive control, applications in the form of training or brain stimulation will improve, and we may be able to intervene to help improve cognitive control. In the meantime, the lessons of control suggest that the best way to keep our control sharp is simply to keep using it. Though we may not be able to improve cognitive control across the board, we can certainly improve it in specific domains or types of tasks. So, learning new skills, like digital photography or woodworking, can be both rewarding and feasible, and can be a source of new independence and support, even as we age. This is not a get-smart-quick scheme, and it won’t make us better multitaskers. However, continually engaging our control systems will help us to better use them as a source of support, compensation, and agency throughout our life.

A primary theme in this chapter has been that cognitive control is a dynamic process. Control function changes continually throughout our life, though the mechanism by which control develops in children is not the same as the mechanism by which it declines in aging adults. Further, the world in which a brain operates is itself constantly changing; it is increasingly unlike the world that brain optimized its control system to confront. Cognitive control is called upon to confront these new challenges and to compensate for them. Thus, its role continues to change even as its own capacities are also changing.

In some sense, it is freeing to recognize that our control resides not simply as an inert faculty in our head but, rather, comprises an ever-changing, ever-adapting interaction between our brain and our world. We are not merely subject to the whims of impenetrable biological processes inside our skull and outside our reach. Indeed, we are agents equipped with systems that allow us to constantly change and adapt to our circumstances. As scientists come to better understand the relationship between the world and the brain, I believe we will be able to intervene in ways that allow people to maintain that sense of agency throughout their life.

CHAPTER 10

Postscript

GETTING THINGS DONE THAT MATTER

How does our brain get things done? In this book we have sought answers to this question across diverse settings and demands. We started with the mystery of a cup of coffee. We are still a long way from a complete answer, but the cognitive neuroscience reviewed in this book has offered some important clues.

We have seen that when we make a cup of coffee our brain confronts the problem of translating a goal, generated by our system for imagining detailed future scenarios, to a sequence of actions that result in that cup of coffee. That sequence of actions is built out of a library of learned response pathways—some simple and others complex. But the timing and order of those response procedures are kept on track by input from task demands held in working memory.

Importantly, for a task like making coffee, we must manage multiple task demands that differ in their level of abstraction and the timescale over which they operate. For example, we maintain the overall task demand of having coffee, along with more intermediate goals like finding sugar or filling the carafe. Further, we must decide when these demands should influence our actions. If we do this incorrectly, we can be captured by habitual routines, putting the mug away before filling it, or doing another task entirely, like losing focus and checking our email. We further use these task demands to guide our memories in service of our goal; remembering that we put the spoons in a different place yesterday might require an active search of our recent past, a retrieval plan, and a decision about the value of retrieved details.

Gates on working memory, operated by interactions between the basal ganglia and frontoparietal cortical networks, are central to this process. Some gates are selective, while others like the stopping system are global and fast. The gates also connect what we do, to value. We might hold a context like finding the sugar in mind because we value that sweet taste. We might also stop ourselves from putting sugar in the coffee today because we value avoiding health problems more. Regardless, our control system is influenced by our estimate not only of the future benefit of this task but of the effort we will exert completing it. Indeed, we might end up with sugar in our coffee not just because we have a sweet tooth but because the mental costs of self-control outweigh the hypothetical health benefits. Thus, to answer how the brain gets things done, we have considered the mechanisms by which our control systems translate what we want into what we do.

A question we haven’t addressed directly, however, is why we should care. If you picked this book up and have reached this point, then you already care at some level. Maybe you are just curious about the brain or the mind, and like me, you have always wondered why and how we do the things we do in the particular ways we do them; nature’s solutions to its problems are so often breathtaking. Or perhaps you are hoping that equipped with more knowledge about how control systems work in general, you may be able to better apply cognitive control in your own life and get some things done!

Regardless of your motivations, however, it is important to consider how what we have learned informs the way we think about our lives and our interactions with one another as a society. For this question, I’d argue that the implications of what we have covered in this book are profound, indeed. Consider a current, particularly salient case.

As I put the last touches on this book, humanity is facing arguably the most significant human caused global threat since the advent of nuclear weapons: the crisis of anthropogenic climate change. There is widespread consensus by scientists that the world is warming at an unprecedented rate, and the cause of this warming is the release of greenhouse gases, like carbon dioxide from fossil fuels, into the atmosphere by human beings. Yet, despite this consensus, our inaction on this issue has been conspicuous.

In 1988, climate scientist James Hansen gave a stern warning in testimony before the US Congress: “The greenhouse effect has been detected and it is changing our climate now.” Again, that was in the late 1980s! Needless to say, we didn’t heed Hansen’s warning. In the three decades since, we have emitted nearly 70% of all the carbon dioxide in the atmosphere; that’s more than 2.3 times the combined emissions from all the years that preceded it.[1] And the earth has warmed by around 1 °C (1.8 °F). Climate scientists anticipate that without significant worldwide reductions in carbon dioxide emissions, the path we are presently on will lead to much greater warming by the end of the twenty-first century, with the highest projections at 2.4 °C–4.7 °C (4.3 °F–8.5 °F).[2] And once we pass that point, those changes may not be reversible for millennia.

The picture is not a pretty one. In the context of this book, however, I want to highlight a point about the human dimension to this catastrophe. If we ultimately manage to avoid this crisis—or if we survive as a species in the event that we don’t—that success will be partly due to our cognitive control system. Consider that we are a species that can envision the risks we are now facing and the hypothetical futures we might be headed toward. We are a species that can innovate and engage in new behaviors that our ancestors have never performed before, as a consequence of imagining those hypothetical futures. Indeed, we can adapt to changes in our environment within the span of a single human lifetime. This adaptability and innovation are enabled by our cognitive control system. So, if lifestyle changes are required to prevent our changing climate or if we must rapidly adapt to a radically altered ecosystem, our cognitive control system will be the reason we are able to do so. It follows, then, that understanding cognitive control may help us think about our agency in this problem.

By now, I hope I’ve convinced you that there is a significant gap to be bridged between knowledge and action. It is not sufficient that we have a goal in mind or a plan or a vision of a better reality. We need control systems to link these fantasies to actions, to monitor our progress, to evaluate the outcomes, and to continually adjust what we are doing. In the case of the human brain, this connection is bridged by our metaphorical gates that regulate working memory. Effective control is the result of good gating policies that balance stability and flexibility and that maximize benefits while minimizing costs, given the particulars of our brain in its world.

This same disconnect between knowledge and action is at the heart of our society’s confusion and frustrations about climate change, particularly our inaction on this issue. In many discussions around climate change, a lot of blame is placed on the so-called climate-change deniers—the people who, despite the scientific evidence and worldwide consensus, still insist that climate change either is not real or is not human caused or won’t ultimately result in bad outcomes, even if the first two points are true. That such people still fight against change is no doubt a problem, particularly as many of these people are in positions of power and are kept there by vested interests in the status quo. Justifiably, a lot of effort has been devoted either to persuading climate deniers to face the reality of our situation or, failing that, to removing them from their positions of power.

Climate-change deniers are not the whole story, however. According to a CBS News poll conducted in 2019, close to 75% of Americans stated that humans contribute to climate change, including 50% of Republicans.[3] Thus, if this one poll is accurate, a lot of people believe in climate change and think humans are responsible. The question that perplexes activists, then, is not why do some people not believe in climate change but, rather, why aren’t the large number of people who do believe in it doing more about it?

This paradox is an example of the knowledge-action dissociation. It is a necessary but not sufficient precondition that people know about climate change and can envision scenarios where the world does or does not avoid this crisis. We can still make gains by educating and informing people about climate change and winning over the skeptics and deniers. However, knowing about climate change and envisioning its effects are not sufficient. We also need to bridge what we know with how we act—a control problem. As such, it is likely that many of the trade-offs we have discussed in this book apply to action on climate change, as well. Let’s delve into some examples to see how.

As governments remain paralyzed by debates and half measures, activists are increasingly emphasizing individual actions people can take to prevent climate change. Driving electric vehicles, reducing air travel, and eating less red meat are all effective ways to reduce an individual’s carbon footprint. If we all did them, we could significantly reduce worldwide emissions and fend off the worst. Yet, the adoption of these actions is still not widespread and heavily lags the rates of belief in climate change. Why?

One reason might be that making these changes in one’s life individually can be difficult, and not just financially. Fossil-fuel use is ubiquitous in our society. Since the Industrial Revolution, our reliance on this kind of energy has become progressively embedded in many of our everyday activities. To really reduce one’s contribution to greenhouse gas emissions, the regular habit and routine of one’s life needs to change, likely radically, and this is not an easy task. As we saw in chapter 7, it is cognitively effortful to make these kinds of changes to established life patterns. To favor flexibility over stability in this case will be mentally costly to people, beyond any financial expense.

To change their life, people need to see a positive trade-off in the benefits of that action versus the cost of engaging in the effort. The benefit of a future where we have avoided radical, damaging changes to our planet’s climate and ecosystems might be enough of a payoff for some people, but given the mass inaction among people who recognize the likelihood of that future, there must be more to the story. For example, if people are skeptical that their individual actions might lead to a desired outcome, even if the outcome itself is valuable, they will discount the outcome based on its likelihood. They may not believe that their individual choice to stop flying or become a vegetarian will stop a warming planet. They will further discount that outcome if the actions to take are themselves costly, as they are in this case. Thus, motivating people will require more than highlighting the severity of our situation. Activists will also need to explicitly emphasize the efficacy of the individual actions they are recommending for achieving the goal of reversing climate change. The bigger the cost of the action, the more it will need to be seen as efficacious.

The problem of climate change is particularly challenging in this regard because it is a classic example of what economists refer to as the tragedy of the commons; that is, a common resource, in this case our planet, is being affected by individual interests to the detriment of the whole. Individuals taking action to reduce that impact and conserve that resource leave space for other individuals only to exploit the common resource all the more. So, though some people are making major personal sacrifices in an effort to reduce emissions—and this is commendable—ultimately, the solution may be that governments pass laws enforcing reductions for everyone on the scale needed for climate change.

This is why ideas like a carbon tax make sense to me, personally. To be clear, I am not an expert on public policy. My preference for a carbon tax or similar solutions is mostly an opinion informed by people whom I trust to have studied it more. Nonetheless, solutions like a carbon tax make sense to me based on what I do know about the trade-offs that cognitive control confronts.

A carbon tax makes the costs of not changing behavior explicit and immediate, by making fossil-fuel use more expensive. This will affect prices, not just for the gas we put in our car or fuel for air travel, but for fossil fuels used everywhere else, for example, in manufacturing and industry. That will price consumption appropriately. Thus, people will no longer be balancing the costs of major life changes against a future hypothetical benefit but, rather, against the tangible and immediate financial costs of staying with a behavior. That trade-off will be more likely to favor flexibility over stability. Similarly, businesses needing to reduce their costs will be motivated to look for alternatives to fossil fuels and will make the investments necessary to save money over the long term. Incidentally, a side effect will be a big market for high-value alternatives to fossil fuels that will make these shifts even easier. In summary, then, a carbon tax shifts the stability-flexibility trade-off in favor of flexibility by modifying the reward structure.

Of course, in this line of argument, we are talking about flexibility versus stability of a society, not just an individual. Is that a reasonable leap to make from our discussion of cognitive control in the human brain? I think the answer is yes. Many aspects of control discussed in this book are fundamental. Whether stability versus flexibility, generality versus specificity, informativity versus accuracy, or simultaneity versus opportunity, these trade-offs are found not just in our brain but in any system that is highly general and needs to control the states in which it finds itself. A human civilization is certainly such a system. As a society, if we are to succeed in our efforts to save ourselves and our planet from climate change, we will need to confront these dilemmas, just as the brain does, and work to reach a better future.

The good news is that cognitive control means we always have a choice. Humans can dream about amazing and fanciful futures. We can come up with almost any norm or system of rules our society wants to live by. These new rules may have never even occurred to our ancestors, but our cognitive control system means we have a chance to make those fantasies real. Just take as an example how rapidly and radically society changed in the face of the COVID-19 (novel coronavirus) pandemic. This is a conception of free will, but it doesn’t require that we deny our fundamental physical and biological nature. Of course, some ideas, plans, norms, and rules we invent will be hard for us to realize because of our individual nature. Since we are physical beings with a particular configuration of organs, genes, and experiences, certain goals will be more challenging than others. But we don’t have to be a blank slate to pursue societal change, and conversely, we don’t have to rule out any ideas a priori because they are in some way against our nature. Cognitive control means we can find a way to make real what we conceive despite our constraints. When we succeed, we connect how we live with how we wish to live. We can get anything done, and what could be more liberating than that?

NOTES

Chapter 1

What Lies in the Gap between Knowledge and Action?

1. 1. http://heartmindonline.org/resources/10-exercises-for-your-prefrontal-cortex.

2. 2. James, W. (1890). The Principles of Psychology. New York: Henry Holt and Company.

3. 3. Hebb, D. O. (1945). Man’s frontal lobes. Archives of Neurology & Psychiatry 54, 10–24.

4. 4. Others, notably Ackerly, later disputed Hebb’s account, citing starkly different reports from relatives that the patient had to live with his family and was unable to so much as dress himself in the morning without help.

5. 5. Penfield, W., and Evans, J. (1935). The frontal lobe in man: A clinical study of maximum removals. Brain: A Journal of Neurology 58, 115–133.

6. 6. Eslinger, P. J., and Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal ablation: Patient EVR. Neurology 35, 1731–1741.

7. 7. Benson, D. F. (1994). Neurology of Thinking. New York: Oxford University Press.

8. 8. Burgess, P. W., Alderman, N., Evans, J., Emslie, H., and Wilson, B. (1998). The ecological validity of tests of executive function. Journal of the International Neuropsychological Society 4, 547–558.

9. 9. Royall, D. R., Lauterbach, E. C., Cummings, J. L., Reeve, A., Rummans, T. A., Kaufer, D. I., LaFrance, W. C., Jr., and Coffey, C. E. (2002). Executive control function: A review of its promise and challenges for clinical research. A report from the Committee on Research of the American Neuropsychological Association. Journal of Neuropsychiatry and Clinical Neuroscience 14(4), 377–405.

10. 10. Chaytor, N., Schmitter-Edgecombe, M., and Burr, R. (2006). Improving ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology 21(3), 217–227.

11. 11. Shallice, T., and Burgess, P. W. (1991). Deficits in strategy application following frontal lobe damage in man. Brain 114, 727–741.

12. 12. Milner, B. (1964). Some effects of frontal lobectomy in man. In J. M. Warren and K. Akert (eds.), The Frontal Granular Cortex and Behavior. New York: McGraw-Hill, 313–334.

13. 13. See note 7.

14. 14. Devinsky, O., and D’Esposito, M. (2004). “Executive Function and the Frontal Lobes.” In Neurology of Cognitive and Behavioral Disorders. New York: Oxford University Press, 302–329.

15. 15. Lhermitte, F., Pillon, B., and Serdarv, M. (1986). Human autonomy and the frontal lobes. Part I: Imitation and utilization behavior: A neuropsychological study of 75 patients. Annals of Neurology 19, 326–334; Lhermitte, F. (1986). Human autonomy and the frontal lobes. Part II: Patient behavior in complex and social situations: “The environmental dependency syndrome.” Annals of Neurology 19, 335–343.

16. 16. See note 2.

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19. 19. See note 10.

Chapter 2

The Origins of Human Cognitive Control

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6. 6. Note that the use of generalist intelligence here should not be confused with the psychometric term general intelligence, or IQ. Here it is intended to distinguish it from a specialist intelligence. We will discuss the relationship between the IQ sense of general intelligence and cognitive control in chapter 9.

7. 7. Kansky, K., Silver, T., Mely, D. A., Ekdawy, M., Lazaro-Gredilla, M., Lou, X., Dorfman, N., Szymon, S., Phoenix, S., and George, D. (2017). Schema Networks: Zero-shot transfer with a generative causal model of intuitive physics. In Proceedings of Machine Learning Research. Vol. 70: International Conference on Machine Learning, Sydney, Australia, August 6–11.

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13. 13. Zeithamova, D., Dominick, A. L., and Preston, A. R. (2014). Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference. Neuron 75(1), 168–179.

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17. 17. https://www.innocenceproject.org.

18. 18. This hypothesis is argued more completely in Schacter et al. (2011); see note 16.

19. 19. Schacter, D. L., Addis, D. R., and Buckner, R. L. (2007). Remembering the past to imagine the future: The prospective brain. Nature Reviews Neuroscience 8, 657–661; Buckner, R. L., and Carroll, D. C. (2007). Self-projection and the brain. Trends in Cognitive Sciences 11(2), 49–57.

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32. 32. Reviewed in note 25.

33. 33. Stringer C. (2007). “The Origin and Dispersal of Homo sapiens: Our current State of Knowledge.” In P. Mellars, K. Boyle, O. Bar Yosef, C. Stringer (eds.), Rethinking the Human Revolution. McDonald Institute for Archaeological Research, Cambridge; McDougall I., Brown, F. H., and Fleagle, J. G., (2007). Stratigraphic placement and age of modern humans from Kibish, Ethiopia. Nature 433, 733–736.

34. 34. Coolidge, F. L., and Wynn, T. (2009). The Rise of Homo sapiens: The Evolution of Modern Thinking. West Sussex, UK: Wiley-Blackwell.

35. 35. Ibid.; Diamond, J. (1992). The Third Chimpanzee: The Evolution and Future of the Human Animal. New York: Harper Collins; Ramachandran, V. (2003). The Emerging Mind. London: Profile Books.

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37. 37. Ambrose, S. H. (2001). Paleolithic technology and human evolution. Science 291, 1748–1753.

38. 38. E. Trinkaus, and H. Shang, (2008). Anatomical evidence for the antiquity of human footwear: Tianyuan and Sunghir. Journal of Archaeological Science 35, 1928–1933

39. 39. T. Wynn, Coolidge, F., and Bright, M. (2009). Hohlenstein-Stadel and the evolution of human conceptual thought.” Cambridge Archaeological Journal 19(1), 73–83.

40. 40. Coolidge, F. L., and Wynn, T. (2005). Working memory, its executive functions, and the emergence of modern thinking. Cambridge Archaeological Journal 15(1), 5–26.

41. 41. Stout, D., Apel, J., Commander, J., and Roberts, M. (2014). Late Acheulean technology and cognition at Boxgrove, UK. Journal of Archeological Science 41, 576–590.

42. 42. Ólafsdóttir, H. F., Bush, D., and Barry, C. (2018). The role of hippocampal replay in memory and planning. Current Biology 28(1), R37–R50.

43. 43. Jerison, H. (1977). The theory of encephalization. Annals of the New York Academic of Sciences 299, 146–160.

44. 44. Striedter, G. (2005). Principles of Brain Evolution. Sunderland, MA: Sinauer.

45. 45. Stephan, H., Frahm, H., and Baron, G. (1981). New and revised data on volumes of brain structures in insectivores and primates. Folia Primatology 35, 1–29.

46. 46. Donahue, C. J., Glasser, M. F., Preuss, T. M., Rilling, J. K., and Van Essen, D. C. (2018). Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates. Proceedings of the National Academy of Sciences, USA 115(22), E5183–5192.

47. 47. Finlay, B. L. (2016). Principles of network architecture emerging from comparisons of the cerebral cortex in large and small brains. PLoS Biology 14, e1002556; Thiebaut de Schotten, M., Urbanski, M., Benedicte, B., Levy, R., Dubois, B., Cerliani, L., and Volle, E. (2017). Rostro-caudal architecture of the frontal lobes in humans. Cerebral Cortex 27, 4033–4047.

48. 48. Sanides, F. (1972). “Representation of the Cerebral Cortex and Its Areal Lamination Pattern.” In G. H. Bourne (ed.), The Structure and Function of the Nervous System. New York: Academic Press, 329–453; Yeterian, E. H., Pandya, D. N., Tomaiuolo, F., and Petrides, M. (2012). The cortical connectivity of the prefrontal cortex in the monkey brain. Cortex 48(1), 58–81.

49. 49. Van Essen, D. C., and Dierker, D. L. (2007). Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56, 209–225.

50. 50. Raichle, M. (2006). The brain’s dark energy. Science 314, 1249–1250.

51. 51. Rosati, A. G. (2017). Foraging cognition: Reviving the ecological intelligence hypothesis. Trends in Cognitive Sciences 21(9), 691–702.

52. 52. Tomlin, D., Rand, D. G., Ludvig, E. A., and Cohen, J. D. (2015). The evolution and devolution of cognitive control: The costs of deliberation in a competitive world. Scientific Reports 5, 11002; Rand, D. G., Tomlin, D., Bear, A., Ludvig, E. A., and Cohen, J. D. (2017). Cyclical population dynamics of automatic versus controlled processing: An evolutionary pendulum. Psychological Review 124(5), 626–642.

Chapter 3

The Stability-Flexibility Dilemma

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4. 4. See note 2.

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16. 16. Badre, D. (2008). Cognitive control, hierarchy, and the rostro-caudal axis of the frontal lobes. Trends in Cognitive Science 12(5), 193–200.

17. 17. Mink, J. W. (1996). The basal ganglia: Focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381–425.

18. 18. Alexander, G. E., Delong, M. R., and Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience 9, 357–81; Haber, S. N. (2003). The primate basal ganglia: Parallel and integrative networks. Journal of Chemical Neuroanatomy 26(4), 317–330.

19. 19. Bayer, H. M., and Glimcher, P. W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron 47, 129–141; Arias-Carrión, O., Stamelou, M., Murillo-Rodríguez, E., Menéndez- González, M., and Pöppel, E. (2010). Dopaminergic reward system: A short integrative review. International Archives of Medicine 3, 24.

20. 20. Shen, W., Flajolet, M., Greengard, P., and Surmeier, D. J. (2008). Dichotomous dopaminergic control of striatal synaptic plasticity. Science 321, 848–851; Surmeier, D. J., Ding, J., Day, M., Wang, Z., and Shen, W. (2007). D1 and D2 dopamine-receptor modulation of striatal glutamatergic signaling in striatal medium spiny neurons. Trends in Neurosciences 30, 228–235.

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22. 22. See Alexander, Delong, and Strick (1986) in note 17.

23. 23. O’Reilly, R. C., and Frank, M. J. (2006). Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation 18(2), 283–328.

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Chapter 4

Hierarchies in the Head

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4. 4. Lashley, K. S. (1951). “The Problem of Serial Order in Behavior.” In L. A. Jeffress (ed.), Cerebral Mechanisms in Behavior. New York: John Wiley and Sons, 112–146.

5. 5. Ibid., 117.

6. 6. Reason, J. (1990). Human Error. Cambridge: Cambridge University Press.

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12. 12. Collins, A. G., and Frank, M. J. (2013). Cognitive control over learning: Creating, clustering, and generalizing task-set structure. Psychological Review 120(1), 190–229.

13. 13. Duncan, J., Chylinski, D., Mitchell, D. J., and Bhandari, A. (2017). Complexity and compositionality in fluid intelligence. Proceedings of the National Academy of Sciences, USA 114(20), 5295–5299.

14. 14. Badre, D., and Nee, D. E. (2018). Frontal cortex and the hierarchical control of behavior. Trends in Cognitive Science 22(2), 170–188.

15. 15. See note 11.

16. 16. Koechlin, E., Ody, C., and Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185; Badre, D., and D’Esposito, M. (2007). Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience 19(12), 2082–2099.

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24. 24. Verstynen, T. D., Badre, D., Jarbo, K., and Schneider, W. (2012). Microstructural organizational patterns in the human corticostriatal system. Journal of Neurophysiology 107(11), 2984–2995.

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Chapter 5

The Tao of Multitasking

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Chapter 6

Stopping

1. 1. Plato. Phaedrus (sections 246a–254e).

2. 2. https://www.businessinsider.com/habits-of-successful-ceos-2014-10.

3. 3. James, W. (1890). The Principles of Psychology. New York: Henry Holt and Company.

4. 4. Freud, “Preface and footnotes to the translation of Charcot’s Tuesday Lectures,” 138. As cited in Smith (1992); see note 5.

5. 5. For example, Sherrington, C. S. (1925). Remarks on some aspects of reflex inhibition. Proceedings of the Royal Society B: Biological Sciences 97(686), 519–545; Smith, R. (1992). Inhibition: History and Meaning in the Sciences of Mind and Brain. Berkeley, CA: University of California Press.

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7. 7. Miller, G. A., Galanter, E., and Pribram, K. H. (1960). Plans and the Structure of Human Behavior. New York: Holt, Rinehart and Winston.

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11. 11. Ibid., 97.

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14. 14. See chapter 5, note 17.

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19. 19. Aron, A. R., Cai, W., Badre, D., & Robbins, T. W. (2015). Evidence supports specific braking function for inferior PFC. Trends in Cognitive Sciences 19(12), 711–712.

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21. 21. Aron et al. (2016); see note 16.

22. 22. Wessel, J. R., and Aron, A. R. (2017). On the globality of motor suppression: Unexpected events and their influence on behavior and cognition. Neuron 93(2), 259–280.

23. 23. See note 22.

24. 24. Li, C. S., Yan, P., Sinha, R., and Lee, T. W. (2008). Subcortical processes of motor response inhibition during a stop signal task. NeuroImage, 41(4), 1352–1363; Schmidt, R., Leventhal, D. K., Mallet, N., Chen, F., and Berke, J. D. (2013). Canceling actions involves a race between basal ganglia pathways. Nature Neuroscience 16(8), 1118–1124.

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26. 26. Reviewed in Wessel and Aron (2017); see note 22.

27. 27. Schacter, D. (2001). The Seven Sins of Memory. Boston: Houghton Mifflin.

28. 28. Wegner, D. M., Schneider, D. J., Carter, S., and White, T. (1987). Paradoxical effects of thought suppression. Journal of Personality and Social Psychology 53, 5–13.

29. 29. Frank, M. J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T. V., Cavanagh, J. F., and Badre, D. (2015). fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. Journal of Neuroscience 35(2), 485–494.

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31. 31. Anderson, M. C., and Spellman, B. A. (1995). On the status of inhibitory mechanisms in cognition: Memory retrieval as a model case. Psychological Review 102, 68–100; Anderson, M. C., and Green, C. (2001). Suppressing unwanted memories by executive control. Nature 410, 366–369.

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Chapter 7

The Costs and Benefits of Control

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4. 4. Vaidya, A. R., Sefranek, M., and Fellows, L. K. (2018). Ventromedial frontal lobe damage alters how specific attributes are weighed in subjective valuation. Cerebral Cortex 28(11), 3857–3867.

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7. 7. Dixon, M.L., and Christoff, K. (2012). The decision to engage cognitive control is driven by expected reward-value: neural and behavioral evidence. PLOS ONE 7:e51637; Etzel, J. A., Cole, M. W., Zacks, J. M., Kay, K. N., and Braver, T. S. (2016). Reward motivation enhances task coding in frontoparietal cortex. Cerebral Cortex 26(4), 1647–1659.

8. 8. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology 80, 1–27.

9. 9. Montague, P. R., Dayan, P., and Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience 16(5), 1936–47; Schultz, W., Dayan, P., and Montegue, P. R. (1997). A neural substrate of prediction and reward. Science 275, 1593–1599.

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11. 11. Surmeier, D. J., Ding, J., Day, M., Wang, Z., and Shen, W. (2007). D1 and D2 dopamine-receptor modulation of striatal glutamatergic signaling in striatal medium spiny neurons. Trends in Neurosciences 30, 228–235.

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13. 13. Watanabe, M., Kodama, T., Hikosaka, K. (1997). Increase of extracellular dopamine in primate prefrontal cortex during a working memory task. Journal of Neurophysiology 78(5), 2795–2798.

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16. 16. Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., and Bigham, J. P. (2018). A data-driven analysis of workers’ earnings on Amazon Mechanical Turk. Paper No. 499 in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, April 21–26.

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18. 18. See note 16.

19. 19. Kool, W. and Botvinick, M. (2014). A labor/leisure tradeoff in cognitive control. Journal of Experimental Psychology General 143, 131–141.

20. 20. Bettmann, S. (1892). Über die Beeinflussung einfacher psychischer Vorgänge durch körperliche und geistige Arbeit. Psychologische Arbeiten 1, 152–208.

21. 21. Thorndike, E. (1900). Mental fatigue. I., Psychological Review 7(6), 547–579.

22. 22. Arai, T. (1912). Mental Fatigue. New York: Teachers College Press, Columbia University.

23. 23. Huxtable, Z. L., White, M. H., and McCartor, M. A. (1946). A re-performance and re-interpretation of the Arai experiment in mental fatigue in three subjects. Psychological Monographs 59(5), 1–52.

24. 24. https://www.vanityfair.com/news/2012/10/michael-lewis-profile-barack-obama.

25. 25. Baumeister, R. F., Bratlavsky, E., Muraven, M., and Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology 74(5), 1252–1265.

26. 26. Kurzban, R., Duckworth, A., Kable, J. W., and Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences 36(6), 661–679.

27. 27. Kurzban, R., Duckworth, A., Kable, J. W., and Myers, J. (2013). Cost-benefit models as the next, best option for understanding subjective effort. Behavioral and Brain Sciences 36(6), 707–726.

28. 28. Muraven, M., Shmueli, D., and Burkley, E. (2006). Conserving self-control strength. Journal of Personality and Social Psychology 91(3), 524–37; Muraven, M., Slessareva, E. (2003). Mechanisms of self-control failure: Motivation and limited resources. Personality and Social Psychology Bulletin 29(7), 894–906; Baumeister, R. F., and Vohs, K. D. (2007). Self-regulation, ego depletion, and motivation. Social and Personality Psychology Compass 1(1), 115–28.

29. 29. Hagger, M. S., Wood, C., Stiff, C., and Chatzisarantis, N.L.D. (2010). Ego depletion and the strength model of self-control: A meta-analysis. Psychological Bulletin 136(4), 495–525.

30. 30. Carter, E. C., and McCullough, M. E. (2013). Is ego depletion too incredible? Evidence for the overestimation of the depletion effect. Behavioral & Brain Sciences 36, 683–684.

31. 31. Carter, E. C., Kofler, L. M., Forster, D. E., and McCullough, M. E. (201). A series of meta-analytic tests of the depletion effect: Self-control does not seem to rely on a limited resource. Journal of Experimental Psychology: General 144, 796–815.

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34. 34. Vadillo, M. A., Gold, N., and Osman, M. (2018). Searching for the bottom of the ego well: Failure to uncover ego depletion in Many Lab 3. Royal Society Open Science, https://doi.org/10.1098/rsos.180390.

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36. 36. Dang, J. (2016). Testing the role of glucose in self-control: A meta-analysis. Appetite 107, 222–230.

37. 37. Holroyd, C. (2016). “The Waste Disposal Problem of Effortful Control.” In T. S. Braver (ed.), Motivation and Cognitive Control. New York: Routledge, 235–260.

38. 38. Smallwood, J., and Schooler, J. W. (2015). The science of mind wandering: Empirically navigating the stream of consciousness. Annual Review of Psychology 66, 487–518.

39. 39. Christoff, K., Gordon, A. M., Smallwood, J., Smith, R., and Schooler, J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences, USA, 106, 8719–8724.

40. 40. Marchetti, I., Koster, E.H.W., Klinger, E., and Alloy, L. B., (2016). Spontaneous thought and vulnerability to mood disorders: The dark side of the wandering mind. Clinical Psychological Science 4(5), 835–57.

41. 41. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., and Cohen J. D. (2001). Conflict monitoring and cognitive control. Psychological Review 108(3), 624–652.

42. 42. Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., and Cohen J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402, 179–181.

43. 43. Ito, S., Stuphorn, V., Brown, J. W., and Schall, J. D. (2003). Performance monitoring by the anterior cingulate cortex during saccade countermanding. Science 302, 120–122; Hyman, J. M., Whitman, J., Emberly, E., Woodward, T. S., and Seamans, J. K. (2013). Action and outcome activity state patterns in the anterior cingulate cortex. Cerebral Cortex 23(6), 1257–1268.

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46. 46. Modirrousta, M., and Fellows, L. K. (2008). Dorsal medial prefrontal cortex plays a necessary role in rapid error prediction in humans. Journal of Neuroscience 28(51), 14000–14005.

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49. 49. Shenhav, A., Botvinick, M. M., and Cohen, J. D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron 79(2), 217–240.

Chapter 8

The Information Retrieval Problem

1. 1. Griffiths, T. L., Steyvers, M., and Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science 18(12), 1069–1076.

2. 2. Incidentally, a great book on this topic is The Twenty-Six Words That Created the Internet by Jeffrey Kosseff. Ithaca, NY: Cornell University Press.

3. 3. Anderson, J. R., and Milson, R. (1989). Human memory: An adaptive perspective. Psychological Review 96, 703–719; Schooler, L.J., and Anderson, J. R. (1997). The role of process in the rational analysis of memory. Cognitive Psychology 32, 219–250.

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12. 12. https://www.businessinsider.com/google-just-effectively-killed-the-im-feeling-lucky-button-2010-9.

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14. 14. Goldsmith, M., Koriat, A., and Weinberg-Eliezer, A. (2002). Strategic regulation of grain size in memory reporting. Journal of Experimental Psychology: General 131(1), 73–95.

15. 15. Brickner (1936); see chapter 6, note 10, p. 160.

16. 16. Ibid., p. 166.

17. 17. Anderson, M. C., and Neely, J. H. (1996). “Interference and Inhibition in Memory Retrieval.” In E. L. Bjork and R. A. Bjork (eds.), Memory. San Diego: Academic Press, 237–313.

18. 18. Öztekin, I., Gungor, N. Z., and Badre, D. (2012). Impact of aging on the dynamics of memory retrieval: A time-course analysis. Journal of Memory and Language 67(2), 285–294.

19. 19. Verde, M. F., & Rotello, C. M. (2007). Memory strength and the decision process in recognition memory. Memory & Cognition 35(2), 254–262.

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21. 21. Badre, D., and Wagner, A. D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia 45, 2883–2901.

22. 22. Martin, R. C., and Cheng, Y. (2006). Selection demands versus association strength in the verb generation task. Psychonomic Bulletin and Review 13(3), 396–401.

23. 23. Thompson-Schill, S. L., Swick, D., Farah, M. J., D’Esposito, M., Kan, I. P., & Knight, R. T. (1998). Verb generation in patients with focal frontal lesions: A neuropsychological test of neuroimaging findings. Proceedings of the National Academy of Sciences, USA 95(26), 15855–15860.

24. 24. Badre, D., and Wagner, A. D. (2007). Left ventrolateral prefrontal cortex contributions to the control of memory. Neuropsychologia 45, 2883–2901.

25. 25. Barredo, J., Öztekin, I., and Badre, D. (2015). Ventral fronto-temporal pathway supporting cognitive control of episodic memory retrieval. Cerebral Cortex 25(4), 1004–1019.

26. 26. https://www.alz.org/alzheimers-dementia/facts-figures.

27. 27. Remy, F., Vayssiere, N., Saint-Aubert, L., Barbeau, E., and Pariente, J. (2015). White matter disruption at the prodromal stage of AD: Relationships with hippocampal atrophy and episodic memory performance. Neuroimage: Clinical 7, 482–492; Hiyoshi-Taniguchi, K., Oishi, N., Namiki, C., Miyata, J., Murai, T., Cichocki, A., and Fukuyama, H. (2015). The uncinate fasciculus as a predictor of conversion from aMCI to Alzheimer’s disease. Journal of Neuroimaging 25(5), 748–753.

28. 28. Lisman, J. E., and Grace, A. A. (2005). The hippocampal-VTA loop: Controlling the entry of information into long-term memory. Neuron 46, 703–713.

29. 29. Braun, E. K., Wimmer, G. E., and Shohamy, D. (2018). Retroactive and graded prioritization of memory by reward. Nature Communications 9(1), 4886.

30. 30. Horvitz, J.C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience 96, 651–656; also see note 28.

31. 31. Shohamy, D., and Adcock, R.A. (2010). Dopamine and adaptive memory. Trends in Cognitive Sciences 14, 464–472; Scimeca, J., and Badre, D. (2012). Striatal contributions to declarative memory retrieval. Neuron 75, 380–392.

32. 32. Dillon, D. G., and Pizzagalli, D. A. (2018). Mechanisms of memory disruption in depression. Trends in Neuroscience 41(3), 137–149.

Chapter 9

Cognitive Control over Lifespan

1. 1. Brickner (1936), pp. 94–95; see chapter 6, note 10.

2. 2. Diamond, A. (2002). “Normal Development of Prefrontal Cortex from Birth to Young Adulthood: Cognitive Functions, Anatomy, and Biochemistry.” In D. T. Stuss and R. T. Knight (eds.), Principles of Frontal Lobe Function. Oxford: Oxford University Press, 466–503.

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6. 6. Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., and Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences, USA 101(21), 8174–8179.

7. 7. Huttenlocher, P. R., and Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology 387(2), 167–178.

8. 8. Marr, D. (1982). Vision. San Francisco: W. H. Freeman.

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Chapter 10

Postscript: Getting Things Done That Matter

1. 1. Aron, A. (2019). The climate crisis needs attention from cognitive scientists. Trends in Cognitive Science 23(11), 903–906.

2. 2. USGCRP (2018). Fourth National Climate Assessment, Volume II: Impacts, Risks, and Adaptation in the United States. Edited by D. R. Reidmiller, C. W. Avery, D. R. Easterling, K. E. Kunkel, K.L.M. Lewis, T. K. Maycock, and B. C. Stewart. US Global Change Research Program, Washington, DC, https://doi.org/10.7930/NCA4.2018.

3. 3. https://www.cbsnews.com/news/cbs-news-poll-most-americans-say-climate-change-should-be-addressed-now-2019-09-15.

INDEX

Page numbers in italics refer to illustrations.

- absentmindedness, 18

- abstraction, 29–30, 49, 75

- Acheulean tools, 33, 34; early vs. late, 51, 52

- addiction, 184–185, 283

- ADHD (attention deficit hyperactivity disorder), 252, 283–285

- aging: cognitive control and, 253, 274–281; compensation and, 281; decision making and, 274; intelligence and, 274–275; memory and, 238–239, 246–247, 274

- akinetic mutism, 187, 189, 212

- altriciality, 58

- Alzheimer’s disease, 4, 209, 246–247

- Amazon Mechanical Turk (MTurk), 198–200

- amnesia, 8, 41, 43, 232

- amphetamine, 184

- amyloid, 209

- A-not-B error, 260–261

- anterior cingulate cortex, 187, 212–220

- anxiety, 211

- aphasia, 8

- Arai, Tsuruko, 201–203, 206

- art, among early hominids, 49

- artificial intelligence (AI), 37–38, 191–192, 194

- asceticism, 158

- assembly lines, 149

- attention, 140–142, 162, 275

- attention deficit hyperactivity disorder (ADHD), 252, 283–285

- austerity, 158

- Australopithecus, 57

- autism, 4, 252

- automatic actions, 21, 67, 108–109; controlled actions vs., 61, 73–74; regulation of, 18; sequential, 17, 105

- axes, 32, 33, 34, 35, 49–51

- back propagation, 70–71

- basal ganglia, 91; aging and, 279; in disinhibitory network, 119, 162; in memory suppression, 181; in motor gating circuit, 90, 93–96, 112, 122, 182, 193, 196

- behavioral economics, 190

- behaviorism, 101, 102–103, 162

- Bettmann, Siegfried, 201

- biased competition, 142–143, 145, 165

- bifaces, 32, 33, 34, 51

- bipedalism, 57

- Bolt, Usain, 168

- The Bourne Identity (film), 41

- brain: aging and, 278; energy needs of, 58; regions vs. networks of, 5, 244, 247; size of, 47, 53–54, 56–58, 256, 278; training to improve, 283–291; tumors in, 6, 7

- branching control structure, 21

- Breakout (computer game), 38, 39

- Brickner, Richard, 163, 236–237

- Broadbent, Donald, 140–141

- Brown, Fred, 157, 168

- Buddhism, 158

- capture errors, 105–109, 111

- carbon tax, 298

- caudal prefrontal cortex, 122–123, 124, 127–128, 255

- caudate, 279

- caudate nucleus, 91

- CDM model, 112, 121; advantages of, 73–74; back propagation in, 70–71; biased competition model likened to, 142–143; diagrams of, 68, 120; layers of, 69–70, 72, 82; limitations of, 119–120, 213; monitoring node added to, 213–214; Stroop test and, 67–69, 71, 72; task difficulty in, 77; working memory in, 75, 79, 94

- cell migration, 255

- cellphones, 19, 66–67, 132–133, 136–137

- chaining theory, 100–104

- chimpanzees, 45, 46, 54–55, 58, 122

- Christianity, 158

- Cicero, Marcus Tullius, 230

- cingulo-opercular network, 77, 78, 212

- circular reasoning, 209

- climate change, 294–295, 296–298

- clinical trials, 287, 288, 290

- clustering, in memory retrieval, 232

- cocaine, 184–185

- coffee problem, 1–2, 98–99, 293

- cognitive control (executive function): abstraction essential to, 148; action distinguished from, 16–17, 59; advantages of, 57, 65, 295, 299; aging and, 253, 274–281; anterior cingulate cortex and, 212–220; automatic actions regulated by, 18; challenges of, 11–12, 26; core knowledge independent of, 8–9; costs vs. benefits and, 186–220; deficits in, 229; development in individuals of, 254–274; episodic future thought vs., 63, 64; error and, 30, 61, 65; evolution of, 30, 34–37, 44, 50, 258–260; fluid intelligence linked to, 275; fragility of, 3–4; frontoparietal network linked to, 77; genetic and environmental factors shaping, 258, 259, 266, 268–271, 289; incentivizing, 192–193; individual differences in, 267–268, 282, 284; language linked to, 39; learning and experience using, 270–273; measurement of, 11–12; memory dependent on, 221, 226, 229, 231, 241–247; memory gating linked to, 95–96; mental fatigue and, 204–205; moral neutrality of, 61; motivation linked to, 30, 185, 188–189, 190, 192–193, 200, 220; multitasking and, 138, 156; popular misconceptions of, 251–253; of sequential tasks, 104, 105, 109; test conditions in, 25; training to improve, 282–291; value and, 190–198. See also gating; inhibition; working memory

- cognitive estimation, 228–229

- cognitive reserve, 281–282

- compensation, by older adults, 281–282

- competition: biased, 142–143, 145, 165; evolutionary, 57, 58, 60, 65; between neural pathways, 59, 72–74, 95, 108–109, 112–113; between responses, 213–214; between tasks, 152–155, 163

- compositionality, 30, 39, 51, 52–53, 57, 118

- computational models, 67–68

- computers: for Internet searches, 226–228, 234–235, 247, 250; memory of, 221–222, 225–226, 228, 247; programs for, 19–21; technological advances due to, 63; widespread use of, 132

- conditional testing, 23, 25

- confabulation, 42, 43–44, 235

- consumption, storage vs., 59–60, 187–188, 232

- context-first/context-last task, 84–87, 270

- control processes, 19–25

- cooperation, 58

- coronavirus, 299

- corticospinal tract, 88

- corticostriatal pathway, 91; fast, global stopping distinguished from, 182–183; Go and NoGo, 92, 169; in reward system, 195; working memory gating in, 94, 96, 122, 123, 128–129, 164, 169, 197–198. See also gating

- corticothalamic drive, 90, 94, 166

- C++, 20

- cranial capacity, 47, 53–54, 56–58, 256, 278

- crystallized intelligence, 275

- cultural transmission, 47, 52, 58

- daydreaming, 210

- “Death Star model,” of cognitive control development, 258–259, 265

- decision making: aging and, 274; branching in, 62; cognitive control in, 18; frontal lobe disorder and, 10; mental fatigue and, 203; by mice, 52; present vs. future needs in, 59–60, 62; schemas used in, 42; speed-accuracy tradeoff in, 176–179; time cost of, 60

- decision trees, 113–118, 124

- deep brain stimulation, 178–179, 183

- Deep Mind, 68, 191–192

- degrees-of-freedom problem, 28–29

- déjà vu, 240

- deliberate practice, 46–47

- dementia, 209

- depression, 211, 249

- diagonal connections, 122, 123, 126

- dimensional change card sorting task, 262

- dimensionality, 28

- disinhibition, 90, 95, 96, 119, 122, 123, 162. See also inhibition

- distracted driving, 66–67, 96–97, 133, 137

- dopamine: aging and, 279; input gating linked to, 96; as Parkinson’s treatment, 178, 183; in prefrontal cortex, 197–198; reward prediction error and, 194–197, 215, 248; stress and, 249; in striatum, 92, 94, 193–198

- dorsolateral prefrontal cortex (DLPFC), 125

- dorsomedial prefrontal cortex, 170

- dualism, 158

- dual systems framework, 59

- dysexecutive behavior, 165, 251–253

- early-selection theory, 141

- Ego, 160

- ego depletion, 204–208

- emotional regulation, 275

- environmental dependency syndrome, 17

- epilepsy, 218

- episodic future thought: anterior prefrontal cortex linked to, 56; in child development, 45–46; cognitive control vs., 63, 64; compositional action control linked to, 52–53; cost of, 58–59; episodic memory linked to, 40, 43, 52; evolution of, 57; language and, 64; in nonhuman species, 44; working memory gating linked to, 96

- episodic memory, 40–41, 43, 74

- Eriksen flanker task, 216, 217

- errors, 13, 76, 83; A-not-B, 260–261; capture, 105–109, 111; cognitive control and, 30, 61, 65; deep decision trees linked to, 116; in developmental tests, 262–263; inhibition vs., 157; learning through, 71; in memory retrieval, 232, 236; in multitasking, 135; perseveration and, 15, 163; response speed following, 216–217; sequential, 105–109; stop-signal delay and, 168; unperceived, 103. See also reward prediction error

- evolution: of cognitive control, 30, 34–37, 44, 50, 258–260; of fallible memory system, 43; of frontal lobe and cortex, 56, 95, 129, 256; of human intelligence, 47; of prefrontal cortex, 93, 95; of working memory, 96

- EVR (patient), 9–10

- executive function. See cognitive control

- extractive foraging, 58

- facial recognition, 153–154

- failure, as learning tool, 272–273

- false memories, 42–43, 239

- family resemblance, of subtasks, 106–107

- far transfer, 286–289

- feedback, 23–24, 91, 92, 116–117

- financial crisis of 2007–2008, 61

- finger movement, 173

- finite-state machines, 151

- fishing, 48

- flexibility, task stability vs., 80–82, 94, 188, 197–198, 267, 296–298

- flint knapping, 32–34, 51

- fluid intelligence, 274–275, 286, 287

- Food and Drug Administration (FDA), 284

- footwear, 48

- foraging, 58

- Ford, Henry, 149

- Ford Motor Company, 149

- foresight, 37, 44–45, 59

- forgetting, 237–238

- fossil fuels, 297, 298

- fovea, 140

- fraternal twins, 267

- Freud, Sigmund, 160–162

- frontal dysexecutive syndrome, 165

- frontal hypothesis, 279, 280

- frontal lobe and cortex, 77, 91, 123, 125, 147, 182, 193; aging and, 279–280; cell migration in, 255; evolution of, 56, 95, 129, 256; hierarchy of, 125–127, 129; injury and surgery to, 4–13, 16, 124, 162–163, 165, 231, 236–237, 242, 254; in memory retrieval, 232; volume loss in, 278

- frontoparietal control system, 78, 170, 171, 244, 286; bottlenecks linked to, 146–148; composition of, 77–78, 124; conflict resolution in, 214; multiple demand system linked to, 77; sensation seeking linked to, 184; during task switching, 153, 154

- functional gradient, 124

- functional magnetic resonance imaging (fMRI), 42

- fusiform gyrus, 153

- future bias, 210

- GABA (neurotransmitter), 269

- Galton, Francis, 202

- galvanic skin response, 217

- gating, 123, 164; aging and, 279; fast stopping system and, 182–183, 294; flexibility and stability balanced by, 81–82, 94, 188, 197–198, 296; higher-order, 127–129; input gating, 82–87, 95–97, 119; in motor circuit, 90–93; output gating, 82–87, 95–97, 119, 122; postretrieval memory control likened to, 235–236; selective vs. global, 94, 169, 171, 174, 182–183, 184, 294; of working memory, 62, 81–88, 94–96, 118–119, 121, 176, 184, 188, 193, 197, 244, 248, 255, 267, 270–271, 279, 284, 294

- generalization, 27

- generativity, 39; of schemas, 42

- globus pallidus (GPi), 90–91, 122, 123, 170, 172, 178

- glucose, 208

- goals: actions vs., 14; episodic memory system linked to, 44; hierarchy of, 51, 52, 56, 57, 64, 98, 100; multiple, 56; points of competition resolved by, 142; prefrontal cortex and, 9–10; reevaluating, 2; in sequential behavior, 100, 102–105; in toolmaking, 52; types of, 2; unfinished, 211

- Go cells, 90–95, 97, 122, 166, 170, 183, 195–197, 279

- go-nogo test, 267

- Google, 19, 68, 191–192, 226–228, 234–235, 247

- gray matter, of cortex, 54, 55, 257

- greenhouse gases, 294–295, 297

- hand axes, 32, 33, 34, 35, 49–51

- Happy Memories (Arai), 202

- Haraguchi, Tsuruko (Arai), 201–203, 206

- head injury, 4

- Hebb, Donald, 5–6

- herding, 48

- heritability, 269–270

- hidden layer, of neural network models, 70

- hierarchical action and control, 22, 24, 38–39, 85, 96–100, 102, 111–112, 124; development in individuals of, 265; in frontal lobe, 125–126; of goals, 51, 52, 56, 57, 64, 98, 100; learning of, 117–118; nested, 121, 128, 129; in prefrontal region, 55–56, 64; of rules, 112–121, 128–129, 262–265; transferability of, 118

- hippocampus, 41–43, 126, 181, 244, 248, 249

- Hixon Symposium (1948), 102

- HM (amnesic patient), 41

- Hohlenstein-Stadel lion man, 49, 50

- Homo erectus, 48

- Homo heidelbergensis, 32–34, 51

- Homo sapiens, 32, 48

- homunculus problem, 26–27, 51, 70

- Human Intelligence Tasks (HITs), 198–200

- hunting, 48, 49, 58

- The Iceman Cometh (O’Neill), 130

- Id, 160

- identical twins, 268

- imitation behavior, 16

- impulsivity, 183, 184–185

- independent living, 274, 276, 277–278

- information retrieval: controlling, 226–234; costs and benefits of, 222–226, 232, 247; as foraging problem, 232. See also memory

- inhibition, 157–185; addiction and, 184–185; aging and, 276; ambiguity surrounding, 163–164; common views of, 157–159; as control process, 162–163, 165; development in individuals of, 261; disinhibition vs., 90, 95, 96, 119, 122, 123, 162; excitatory, 164, 165; in Freudian theory, 160–162; through habit learning, 159–160; heritability of, 270; lateral, 165; measurement of, 167, 226–227, 266; by NoGo pathway, 166, 174; speed of, 168–169, 174–178, 182, 183; through subthalamic nucleus, 169–174, 176, 177–178; of thoughts, 175–180

- Innocence Project, 43

- input gating, 82–87, 95–97, 119

- intelligence: aging and, 274–275; emergence of, 47; generalist vs. specialist, 38; heritability of, 269

- interference, between tasks, 27, 112, 152–156, 163, 264–265

- Internet, 19, 280; searches on, 226–228, 234–235, 247, 250

- introspection, 100

- intrusive thoughts, 175–176, 182

- iPhone, 19

- James, William, 4–5, 18, 19, 159–160, 165

- Jersild, Arthur T., 148–151, 156

- knowledge-action dissociation, 296

- lampreys, 93

- language, 39, 57, 64, 162, 259

- Lashley, Karl, 102–105, 109, 111

- lateral inhibition, 165

- lateral prefrontal cortex, 214, 219, 243

- L-dopa, 196

- Lewis, Michael, 203

- limbic system, 64

- lobotomy, 6

- Long Day’s Journey into Night (O’Neill), 130

- long-term memory, 74, 181, 221, 235–236, 242, 244–245, 248

- lookup tables, 115

- loops, in computer programs, 21, 23

- loss aversion, 190

- macaque monkeys, 55, 56, 75, 194

- machine learning, 280

- Macintosh computer, 19

- Many Labs study (2018), 207–208

- medial prefrontal cortex, 178, 187

- medial temporal lobe, 211, 243, 245

- memory, 162, 221–250; aging and, 238–239, 246–247, 274; amnesic loss of, 8, 41, 43, 232; cognitive control essential to, 221, 226, 229, 231, 241–247; context linked to, 224–225; controlling retrieval of, 226–234; depression and, 249; episodic, 40–41, 43, 74; false, 42–43, 239; forgetting vs., 237–238; hippocampus linked to, 41–43, 126, 181, 244; human vs. mechanical, 221–222, 225–226, 228, 247; information-retrieval problem and, 222–226, 232, 247; interference with, 238–239; intrusive, 175–176; limbic system linked to, 64; limitations of, 221; long-term, 74, 181, 221, 235–236, 242, 244–245, 248; meta-, 234; positivity vs. negativity bias in, 249, 275; postretrieval control of, 226, 234–241, 243–246; of spatial sequences, 285; suppression of, 161, 179–282; thresholds for, 240–241; value and, 247–250. See also information retrieval; working memory

- mental labor, 198–209; budgeting for, 205–206; ego depletion theory and, 204–208

- mental set, 150

- meta-analysis, 288

- meta-memory, 234

- method of loci (“mind palace”), 230–231

- mice, 51, 52, 172

- mid-dorsolateral prefrontal cortex, 125, 126, 127

- migration, by early hominids, 48

- Milner, Brenda, 14–15, 163

- “mind palace” (method of loci), 230–231

- mind wandering, 210

- mnemonics, 230–231

- monkeys, 55, 56, 75, 126, 194

- “Montreal Method,” 6

- motivation, 165, 287; cognitive control linked to, 30, 185, 188–189, 190, 192–193, 200, 220; mental efficacy vs., 201–204; multitasking and, 97, 187–189; in older adults, 275–276, 280; resource depletion and, 205, 206

- motor control, 29, 67, 182, 274, 276–277

- motor cortex, 88, 89, 90, 92, 170, 173–174

- motor potential, 173–174

- MTurk (Amazon Mechanical Turk), 198–200

- multiple demand system, 77

- multitasking, 130–156; biased competition and, 142–143, 145; bottlenecks and, 139–148, 150, 152, 156, 212; brain’s unsuitability for, 138–141, 210; dual-task cost in, 145–148, 155; by early hominids, 51; environments for, 131, 133, 154, 156; as foraging problem, 187–188; growth of, 132; inefficiency of, 134–138; interference between tasks and, 27, 112, 152–156, 163, 264–265; motivation and, 97, 187–189; PRP effect and, 143–145; task- switching cost in, 148–155; training for, 146–147, 156; unpleasantness of, 200, 219

- Musk, Elon, 37

- natural selection, 50

- Neanderthal, 48

- near transfer, 285–288

- negative transfer, 39

- Neisser, Ulrich, 26

- neocortex, 54

- neural network models, 68–69

- “Next” task, 263–265

- Nicks, Elsie, 186–187, 212

- NoGo cells, 90–95, 97, 122, 166, 170, 183, 195–196, 279

- Obama, Barack, 203, 204

- obsessive-compulsive disorder (OCD), 175

- occipital cortex, 89, 142, 279

- Oldowan tools, 33, 34

- O’Neill, Eugene, 130–131, 156

- operation span (OSpan), 136

- opportunity cost, 210, 211–212

- optogenetics, 172, 173

- orangutans, 45

- output gating, 82–87, 95–97, 119, 122

- output grain, in memory retrieval, 236–237

- PageRank, 226–227

- parenting, 272–274

- parietal lobe and cortex, 33, 37, 56, 77, 89, 124, 142. See also frontoparietal control system

- Parkinson’s disease, 4, 153, 178, 183, 195

- patient A, 163, 236–237, 254

- Pavlov, Ivan, 161

- Penfield, Wilder, 6–8

- perception, 28, 64, 76, 89, 145, 162, 274

- perseveration, 15, 163, 262

- Piaget, Jean, 260

- placebo effects, in brain training, 287

- planning: advantages and pitfalls of, 60, 62–63; benign vs. malign, 61; brain disorders and, 10, 40–41; cost of, 58, 61–63, 64; general vs. specific, 10, 40; improvised, 29–30; memory linked to, 36, 41; monitoring linked to, 25; by nonhuman species, 44–45; posterior frontal lobe linked to, 56; toolmaking linked to, 33, 63

- Plato, 157–158

- plausibility, in memory retrieval, 233–234

- Pleistocene, 32, 34, 51

- Pong (computer game), 39

- posterior neocortex, 153

- posterior prefrontal cortex, 56

- posttraumatic stress disorder (PTSD), 175–176

- Predictions of Response and Outcomes (PRO), 215–216

- prefrontal cortex (PFC), 5; anterior mid- dorsolateral, 125, 126, 127; anterior premotor, 125; caudal, 122–123, 124, 127–128, 255; context maintained in, 122; development in individuals of, 56, 57, 64, 256; dopamine’s effect on, 197; dorsolateral, 125; dorsomedial, 170; evolution of, 93, 95; in humans vs. other primates, 54–55, 122; inhibition linked to, 164, 169, 180; injury to, 4–15, 153, 217; lateral, 214, 219, 243; in memory retrieval, 231, 242–244; posterior, 56; rostral, 110–111, 122–128, 255; rostrolateral, 110–111, 125, 126–127; during sequential tasks, 110–111; synaptic change in, 256–257; ventrolateral, 170, 171, 242–244, 246; ventromedial, 42, 43, 126, 191; working memory linked to, 75–76, 81, 94, 95

- premotor cortex, 88–93, 123, 125, 127, 170

- The Principles of Psychology (James), 4–5, 159–160

- process competency, 277

- psychological refractory period (PRP), 143–145

- PTSD (posttraumatic stress disorder), 175–176

- publication bias, 288

- putamen, 91, 279

- Python (computer language), 20

- Raven’s Progressive Matrices test, 286

- reconfiguration, of tasks, 150–152

- reductionism, 27–28

- registered reports, 181

- reinforcement learning, 194, 195

- response conflict, 214–216

- response layer, of neural network models, 70

- retina, 140, 259

- retrieval plans, 228–229

- reward prediction error, 194–197, 216, 248

- RMB (patient), 217–218

- Romano, Ray, 254

- rostrolateral prefrontal cortex (RLPFC), 110–111, 125, 126–127

- routines, 2–3, 8

- rule following, 261–265

- Russel, Colin, 7

- Scaffolding Theory of Aging and Cognition (STAC), 281, 282

- scalability, 27–29, 79, 121

- schemas, 41–42, 43

- search engines, 226–228, 234–235, 247, 250

- seizures, 6

- self-care, 276

- sensation seeking, 184–185

- sensory layer, of neural network models, 69–70

- sequential behavior, 100–112

- The Seven Sins of Memory (Schacter), 176

- Simonides of Ceos, 230

- simultaneity, 210, 212

- smartphones, 19, 132–133, 225, 280

- social media, 66, 132–133

- spatial sequence memory, 285

- spider webs, 35–37

- spinal cord, 88

- STAC (Scaffolding Theory of Aging and Cognition), 281, 282

- stimulus-onset asynchrony (SOA), 143–145

- stop rules, 24

- stop-signal reaction time, 168–169, 171, 184, 266–267, 270

- storage, consumption vs., 59–60, 187–188, 232

- stress, 249, 258

- striatum, 127–129; dopamine in, 92, 94, 193–198; Go and NoGo cells from, 90–92, 94–95, 122, 166, 170, 174; task stability vs. flexibility in, 198; working memory gating and, 96, 255, 279. See also corticostriatal pathway

- stroke, 4, 171, 217, 247, 281

- Stroop test, 76, 95, 112, 163, 214, 215, 267; CDM solution to, 67–69, 71, 72; incentivized performance on, 192–193; limitations of, 27–28; in Many Labs study, 207–208

- structuralist psychology, 100–101

- subjective value, 190

- subprime mortgage crisis, 61

- subroutines, 20, 21

- substantia nigra, 193, 196

- subthalamic nucleus (STN), 170–175, 178–179, 181, 182

- Superego, 160

- supplementary motor area, 88, 92

- surprise, 171–172, 173

- symbolic artifacts, 49

- synaptic weight, 69, 71

- synaptogenesis, 256

- task demand layer, of neural network models, 72, 73

- task stability, flexibility vs., 80–82, 94, 188, 197–198, 267, 296–298

- task-switching cost, 163; in multitasking, 148–155

- temporal abstraction, 29–30, 75

- temporal lobe and cortex, 41, 142, 181, 211, 243, 246

- Test-Operate-Test-Exit (TOTE), structure, 21, 22, 23–26, 162, 213

- texting, 66

- thalamus, 90, 91, 95, 96, 119, 122, 172, 255

- think/no-think tasks, 179–180

- Thorndike, Edward, 201, 202, 203

- time cost, 58–65

- Titchener, Edward, 100–101, 102

- toolmaking, 32–35, 47, 57, 58, 63

- tragedy of the commons, 298

- transcranial magnetic stimulation (TMS), 111, 124, 173–174

- transfer, in problem solving, 39, 87

- Treisman, Anne, 141

- trials, in lab experiments, 29

- “Turkers,” 198–200

- twins, 268–269, 270

- uncinate fasciculus, 246

- updating, 267, 270

- Upper Paleolithic, 47–48, 49, 51

- utilization behavior, 17

- value functions, 190–191

- ventral stem, 246

- ventral tegmental area, 193

- ventral temporal cortex, 142

- ventrolateral prefrontal cortex (VLPFC), 170, 171, 242–244, 246

- ventromedial frontal lobe, 211

- ventromedial prefrontal cortex (VMPFC), 42, 43, 126, 191

- verb generation task, 242

- video games, 286

- vision, 259

- vocabulary, 274–275

- Washburn, Margaret Floy, 101

- Watson, John, 102

- Wechsler Adult Intelligence Scale (WAIS), 9

- white matter, of cortex, 54, 55, 127, 171, 246, 278

- willpower, 161

- Wintersoul, Yanjaa, 231

- Wisconsin Card Sorting Test, 14–15, 163

- witness misidentification, 43

- working memory: abstract concepts in, 148; aging and, 276, 279; in children, 112, 118–119; defined, 50; evolution of, 96; gating of, 62, 81–88, 94–96, 118–119, 121, 176, 184, 188, 193, 197, 244, 248, 255, 267, 270–271, 279, 284, 294; in nonhuman species, 51; small capacity of, 62–63, 74, 79, 81, 235; for task demands, 75–76, 79, 155, 197; temporal abstraction problem bridged by, 75; training to improve, 284–285, 288

- Worthy, James, 157, 168

- writing, 63, 130–131, 156