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Subdivisions of the prefrontal cortex (PFC) evolved at different times. Agranular parts of the PFC emerged in early mammals, and rodents, primates, and other modern mammals share them by inheritance. These are limbic areas and include the agranular orbital cortex and agranular medial frontal cortex (areas 24, 32, and 25). Rodent research provides valuable insights into the structure, functions, and development of these shared areas, but it contributes less to parts of the PFC that are specific to primates, namely, the granular, isocortical PFC that dominates the frontal lobe in humans. The first granular PFC areas evolved either in early primates or in the last common ancestor of primates and tree shrews. Additional granular PFC areas emerged in the primate stem lineage, as represented by modern strepsirrhines. Other granular PFC areas evolved in simians, the group that includes apes, humans, and monkeys. In general, PFC accreted new areas along a roughly posterior to anterior trajectory during primate evolution. A major expansion of the granular PFC occurred in humans in concert with other association areas, with modifications of corticocortical connectivity and gene expression, although current evidence does not support the addition of a large number of new, human-specific PFC areas.

Rodent research provides important insights into neural correlates of human psychology, but new cortical areas, connections, and cognitive abilities emerged during primate evolution, including human evolution. Comparison of human brains with those of nonhuman primates reveals two aspects of human brain evolution particularly relevant to emotional disorders: expansion of homotypical association areas and expansion of the hippocampus. Two uniquely human cognitive capacities link these phylogenetic developments with emotion: (i) a subjective sense of participating in and re-experiencing remembered events; and (ii) a limitless capacity to imagine details of future events. These abilities provided evolving humans with selective advantages, but they also created proclivities for emotional problems. The first capacity evokes the “reliving” of past events in the “here-and-now”, accompanied by emotional responses that occurred during memory encoding. It contributes to risk for stress-related syndromes, such as post-traumatic stress disorder. The second capacity, an ability to imagine future events without temporal limitations, facilitates flexible, goal-related behavior by drawing on and creating a uniquely rich array of mental representations. It promotes goal achievement and reduces errors, but the mental construction of future events also contributes to developmental aspects of anxiety and mood disorders. With maturation of homotypical association areas, the concrete concerns of childhood expand to encompass the abstract apprehensions of adolescence and adulthood. These cognitive capacities and their dysfunction are amenable to a research agenda that melds experimental therapeutic interventions, cognitive neuropsychology, and developmental psychology in both humans and nonhuman primates.

The monkey frontal cortex can flexibly direct covert spatial attention without tapping the circuitry for planning movements., We investigated the spatial representation of covert attention and movement planning in monkeys performing a task that used symbolic cues to decouple the locus of covert attention from the motor target. In the three frontal areas studied, most spatially tuned neurons reflected either where attention was allocated or the planned saccade. Neurons modulated by both covert attention and the motor plan were in the minority. Such dual-purpose neurons were especially rare in premotor and prefrontal cortex but were more common just rostral to the arcuate sulcus. The existence of neurons that indicate where the monkey was attending but not its movement goal runs counter to the idea that the control of spatial attention is entirely reliant on the neuronal circuits underlying motor planning. Rather, the presence of separate neuronal populations for each cognitive process suggests that endogenous attention is under flexible control and can be dissociated from motor intention.

In a major evolutionary transition that occurred more than 520 million years ago, the earliest vertebrates adapted to a life of mobile, predatory foraging guided by distance receptors concentrated on their heads. Vision and olfaction served as the principal sensory systems for guiding their search for nutrients and safe haven. Among their neural innovations, these animals had a telencephalon that included a homologue of the hippocampus. Experiments on goldfish, turtles, lizards, rodents, macaque monkeys and humans have provided insight into the initial adaptive advantages provided by the hippocampus homologue. These findings indicate that it housed specialized map-like representations of odors and sights encountered at various locations in an animal’s home range, including the order and timing in which they should be encountered during a journey. Once these representations emerged in early vertebrates, they also enabled a variety of behaviors beyond navigation. In modern rodents and primates, for example, the specialized representations of the hippocampus enable the learning and performance of tasks involving serial order, timing, recency, relations, sequences of events and behavioral contexts. During primate evolution, certain aspects of these representations gained particular prominence, in part due to the advent of foveal vision in haplorhines. As anthropoid primates—the ancestors of monkeys, apes and humans—changed from small animals that foraged locally into large ones with an extensive home range, they made foraging choices at a distance based on visual scenes. Experimental evidence shows that the hippocampus of monkeys specializes in memories that reflect the representation of such scenes, rather than spatial processing in a general sense. Furthermore, and contrary to the idea that the hippocampus functions in memory to the exclusion of perception, brain imaging studies and lesion effects in humans show that its specialized representations support both the perception and memory of scenes and sequences.

In this chapter, a mummified monkey’s paw causes big trouble; a bushbaby eyes a wicked witch; and a monkey pigs out on fresh berries. But mainly we consider memories that evolved in early primates. As these ancestors adapted to a life in the trees, they became a lot like us. They developed forward-facing eyes and grasping hands; guided reaching movements with vision; moved themselves with their legs; and had a large brain. New cortical areas helped these ancestors survive by using new memories: of how to reach accurately while perched on swaying branches; of the precise amount of force needed to grasp valuable items; of the location and value of items hidden in a clutter of branches and leaves; and of objects and actions linked to a hidden food item’s desirability.

In this chapter, a man with amnesia sparks a new era in memory research; monkeys play a matching game; and a 19th-century politician explains how he seemed so clever. But mainly we consider the nature of memory and amnesia. The ‘man with amnesia’ was named Henry Molaison, better known as H. M., and he suffered from debilitating epilepsy. Brain surgery alleviated his seizures, but it had a shocking side effect: amnesia. His memory loss led scientists down two roads: one exploring the memories he lost; the other identifying the brain areas needed for normal memory. In this book, we consider memory from a different angle, examining how it evolved. We begin with a recent history of memory science and some background material on two important concepts: representation and homology.

We tend to think about memory in terms of our own experience, but a series of our direct ancestors—from the earliest vertebrates to archaic humans—also had memories. The evolutionary history that we share with these ancestral species has left its mark on modern memory, complemented by other forms of memory that are uniquely human. This book traces the long evolutionary road to human memory by explaining how a series of now-extinct ancestral species adapted to life in their world, in their time and place. As they did, new brain areas appeared, each of which supported an innovative form of memory that helped them gain an advantage in life. Through inheritance and modification across millions of years, these evolutionary novelties created several kinds of memory that influence the human mind today. Then, during human evolution, yet another new kind of memory emerged: about ourselves and others. This evolutionary innovation ignited human imagination; empowered us to remember and talk about a personal past; and enabled the sharing of knowledge about our world, our cultures, and ourselves. Through these developments, evolution made it possible for every individual, day upon day, to add new pages to the story of a life: the remarkable, rich record of experiences and knowledge that make up a human mind.

In this chapter, a brainless cockroach has a shocking experience; a giant anemone eats a lawyer; and a drunken sailor considers the danger of dead bees. But mainly we consider the kinds of memories that evolved in early animals. Some of these ancestors didn’t have a brain, but they could still learn about their world. They could, for example, remember that a particular sensation predicted something beneficial or harmful in the near future. After the brain evolved, memories empowered animals to make similar predictions based on what they chose to do, such as where they went for food or safe haven. These predictions enabled early animals to prepare, in advance, for what was about to happen to them and to make choices attuned to their current needs.

In this chapter, a pig wins the Kentucky Derby; people remember bugs and beasts; and monkeys take flight. But mainly we consider memories that evolved in anthropoids. As these ancestors evolved from small animals into larger ones, they needed to travel long distances to obtain food. They used high-power vision and a new kind of color perception to detect distant sights indicating ripe fruit. The memory of these perceptions helped guide their choices toward the most valuable food items both near and far. The sounds made by boisterous, feeding birds provided similar information. Other new memories involved representations of quantities, such as the number of food items and their distance. Eventually, the new memories evolved by anthropoids also helped them recognize and communicate with members of their social group.

In this chapter, a horse talks; pigs fly; and mice design a computer. But mainly we consider a form of memory that evolved in humans. Part of the temporal lobe expanded and added a generalization—representing the concepts and categories of cultural memory—to a specialization: representing distant signs of food. At about the same time, posterior parietal areas also expanded and added a different form of generalization—representing relations of many kinds—to a specialization: representing relations among quantities. Both the temporal and parietal lobes interact with the prefrontal cortex, especially the parts that first emerged in anthropoids. Together, these evolutionary innovations improved the ability of evolving hominins to cope with novel and challenging problems. In this sense, hominin evolution produced cortical areas with a specialization for generalization.

In this chapter, a distant ancestor makes a success of greed, laziness, and fear; a driver hits the jackpot; and Dorothy comes to a fork in the yellow brick road. But mainly we consider how the brain evolved. A succession of forks in our evolutionary road produced new brain areas that are crucial for memory. Each of these areas has its own kind of memory, which evolved in a now-extinct ancestral species long ago. In each case, new forms of memory helped these species survive and thrive in circumstances very different from our own. During human evolution, the brain began expanding about 3 million years ago. It reached its current size and shape about 400,000 years ago, long before the acceleration in cultural innovation that characterizes human societies.

In the epilogue, a couple of kids befriend a shy stegosaurus; a different stegosaurus worries about the rise of mammals; and a tyrannosaurus presents a situation report. But mainly we consider reptilian brains, the relationship of brain size to intelligence, and the evolutionary success of mammals. Contrary to an internet meme, no one has a “reptilian” or “lizard” brain lurking within. Our entire brain is human. Regarding intelligence, brain organization matters as much as brain size and maybe more. Once dinosaurs became extinct, the mammals that supplanted them had much smaller brains than large dinosaurs had. Instead, the success of mammals depended on the emergence of the neocortex, a new part of the brain. Eventually, this evolutionary innovation came to dominate both the brain and the memories that it contains.

In this chapter, Toto chases a cat, so the Wizard of Oz goes home alone; a woman is attacked by her own hand; and a mouse plunders a pantry. But mainly we consider memories that evolved in early mammals. These ancestors developed a new part of the brain called the neocortex, and it changed memory forever. The neocortex endowed early mammals with an ability to manage contradictory memories, each appropriate in certain contexts. Obsolete memories could be retained and used in case the ‘good old days’ returned, and new memories received a boost when it mattered most. The neocortex empowered early mammals to strike a balance among alternative ways of surviving: spending versus conserving energy; acting habitually versus making good choices; and acting with urgency versus patience.

In this chapter, a wizard reads Dorothy’s mind; a hurricane lashes the Florida coast; and Gatsby remembers his long-lost love. But mainly we discuss what makes memories our own. During human evolution, our brains developed a new form of memory that helped support our complex societies. New levels of “self” representation evolved in an expanding cortex, and they empowered people to infer the intentions and mental states of others. Eventually, these new memories became incorporated into older kinds. Embedding memories of “self” into memories of events established participatory memories; and embedding them into memories of facts or fictions established cultural memories. When these memories first evolved, the sense of participating in events and owning knowledge came into the world and made human memory what it is today.

In this chapter, Dorothy braves lions (and tigers and bears); a dinosaur eats someone in Jurassic Park; and a proofreader gets a $7 raise. But mainly we consider how a new form of memory helped anthropoids avoid being eaten. When anthropoids faced a high level of volatility in their preferred foods, such as fruit, they had to make more foraging journeys to get enough to eat. Because they faced a severe threat of predation every time they did, these ancestors benefited from any reduction in the frequency of such excursions. So, they had to establish memories that limited foraging errors, and they had to do so quickly. New cortical areas provided this advantage, so anthropoids survived and thrived in a rapidly changing world teeming with predators.

Comparative neuroanatomy shows that new prefrontal areas emerged during the evolution of anthropoid primates to augment prefrontal, parietal, and temporal areas that had evolved in earlier primates. We recently proposed that the new anthropoid areas reduce foraging errors by generating goals from current contexts and learning to do so rapidly, sometimes based on single events. Among the contexts used to generate these goals, the posterior parietal cortex provides the new prefrontal areas with information about relational metrics such as order, number, duration, length, distance and proportion, which play a crucial role in foraging choices. Here we propose that this specialized network later became adapted to support the human capacity for reasoning and general problem-solving.

Recent research indicates that the orbital prefrontal cortex (PFo) represents stimulus valuations and that the amygdala updates these valuations. An exploration of how PFo and the amygdala interact could improve the understanding of both. PFo and the amygdala function cooperatively when monkeys choose objects associated with recently revalued foods. In other tasks, they function in opposition. PFo uses positive feedback to promote learning in object-reward reversal tasks, and PFo also promotes extinction learning. Amygdala function interferes with both kinds of learning. The amygdala underlies fearful responses to a rubber snake from the first exposure on, but PFo is necessary only after the initial exposure. The amygdala mediates an arousal response in anticipation of rewards, whereas PFo sometimes suppresses such arousal. A role for PFo in advanced cognition, for the amygdala in instinctive behavior, and for cortex-subcortex interactions in prioritizing behaviors provides one account for these findings.

The frontal pole cortex (FPC) expanded markedly during human evolution, but its function remains uncertain in both monkeys and humans. Accordingly, we examined single-cell activity in this area. On every trial, monkeys decided between two response targets on the basis of a 'stay' or 'shift' cue. Feedback followed at a fixed delay. FPC cells did not encode the monkeys' decisions when they were made, but did so later on, as feedback approached. This finding indicates that the FPC is involved in monitoring or evaluating decisions. Using a control task and delayed feedback, we found that decision coding lasted until feedback only when the monkeys combined working memory with sensory cues to 'self-generate' decisions, as opposed to when they simply followed trial-by-trial instructions. A role in monitoring or evaluating self-generated decisions could account for FPC's expansion during human evolution.

SummaryWe examined activity in the frontal cortex as monkeys performed a duration-discrimination task. Two stimuli, one red and the other blue, appeared sequentially on a video screen—in either order. Later, both stimuli reappeared, and to receive a reward the monkeys had to choose the stimulus that had lasted longer during its initial presentation. Some neurons encoded stimulus duration, but a larger number of cells represented their relative duration, which was encoded in three ways: whether the first or second stimulus had lasted longer; whether the red or blue stimulus had lasted longer; or, less commonly, as the difference between the two durations. As the monkeys' choice approached, the signal encoding which stimulus (red or blue) had lasted longer increased as the order-based signal dissipated. By representing stimulus durations and relative durations—both bound to stimulus features and event order—the frontal cortex could contribute to both temporal perception and episodic memory.

The primate prefrontal cortex contributes to stimulus-guided behavior, but the functional specializations among its areas remain uncertain. To better understand such specializations, we contrasted neuronal activity in the dorsolateral prefrontal cortex (PFdl) and the orbital prefrontal cortex (PFo). The task required rhesus monkeys to use a visual cue to choose a saccade target. Some cues instructed the monkeys to repeat their most recent response; others instructed them to change it. Responses were followed by feedback: fluid reward if correct, visual feedback if incorrect. Previous studies, using different tasks, have reported that PFo neurons did not encode responses. We found PFo did encode responses in this task, but only near feedback time, after the response had been completed. PFdl differed from PFo in several respects. As reported previously, some PFdl neurons encoded responses from the previous trial and others encoded planned responses. PFo neurons did not have these properties. After feedback, PFdl encoded rewarded responses better than unrewarded ones and thus combined response and outcome information. PFo, in contrast, encoded the responses chosen, rewarded or not. These findings suggest that PFdl and PFo contribute differently to response knowledge, with PFo using an outcome-independent signal to monitor current responses at feedback time.

The largest part of the primate prefrontal cortex has no homolog in other mammals. Accordingly, it probably confers some advantage that other mammals either lack or attain through the function of other structures. Yet, this advantage remains enigmatic. This is not so for other parts of the cortex. For example, certain visual areas encode, represent and store knowledge about objects. By analogy, perhaps the primate prefrontal cortex encodes, represents and stores knowledge about behaviors, including the consequences of doing (or not doing) something in complex and challenging situations. The long list of functions often attributed to the prefrontal cortex could contribute to knowing what to do and what will happen when rare risks arise or outstanding opportunities knock.

The activity of some prefrontal (PF) cortex neurons distinguishes short from long time intervals. Here, we examined whether this property reflected a general timing mechanism or one dependent on behavioral context. In one task, monkeys discriminated the relative duration of 2 stimuli; in the other, they discriminated the relative distance of 2 stimuli from a fixed reference point. Both tasks had a pre-cue period (interval 1) and a delay period (interval 2) with no discriminant stimulus. Interval 1 elapsed before the presentation of the first discriminant stimulus, and interval 2 began after that stimulus. Both intervals had durations of either 400 or 800 ms. Most PF neurons distinguished short from long durations in one task or interval, but not in the others. When neurons did signal something about duration for both intervals, they did so in an uncorrelated or weakly correlated manner. These results demonstrate a high degree of context dependency in PF time processing. The PF, therefore, does not appear to signal durations abstractedly, as would be expected of a general temporal encoder, but instead does so in a highly context-dependent manner, both within and between tasks.

In healthy humans, the two cerebral hemispheres show functional specialization to a degree unmatched in other animals, and such strong hemispheric specialization contributes to unimanual skill acquisition [1, 2]. When most humans learn a new motor skill with one hand, this process results in performance improvements in the opposite hand as well [3-6]. Despite the obvious adaptive advantage of such intermanual transfer, there is no direct evidence identifying the neural substrates of this form of skill acquisition [7-9]. Here, we used functional magnetic resonance imaging (fMRI) to study brain regions activated during intermanual transfer of a learned sequence of finger movements. First, we found that the supplementary motor area (SMA) has more activity when a skill has transferred well than when it has transferred poorly. Second, we found that fMRI activity in the ventrolateral posterior thalamic nucleus correlated with successful future intermanual transfer, whereas activity in the ventrolateral anterior thalamic nucleus correlated with past intermanual transfer. Third, we found that repetitive transcranial magnetic stimulation applied over the SMA blocked intermanual transfer without affecting skill acquisition. These findings provide direct evidence for an SMA-based mechanism that supports intermanual transfer of motor-skill learning.

The primate prefrontal cortex plays a central role in choosing goals, along with a wide variety of additional functions, including short-term memory. In the present study, we examined neuronal activity in the prefrontal cortex as monkeys used abstract response strategies to select one of three spatial goals, a selection that depended on their memory of the most recent previous goal. During each trial, the monkeys selected a future goal on the basis of events from the previous trial, including both the symbolic visual cue that had appeared on that trial and the previous goal that the monkeys had selected. When a symbolic visual cue repeated from the previous trial, the monkeys stayed with their previous goal as the next (future) goal; when the cue changed, the monkeys shifted from their previous goal to one of the two remaining locations as their future goal. We found that prefrontal neurons had activity that reflected either previous goals or future goals, but only rarely did individual cells reflect both. This finding suggests that essentially separate neural networks encode these two aspects of spatial information processing. A failure to distinguish previous and future goals could lead to two kinds of maladaptive behavior. First, wrongly representing an accomplished goal as still pending could cause perseveration or compulsive checking, two disorders commonly attributed to dysfunction of the prefrontal cortex. Second, mistaking a pending goal as already accomplished could cause the failures of omission that occur commonly in dementia.

In addition to its projection to the brainstem, the ventral premotor cortex (PMv) sends axons directly to the upper cervical spinal cord in primates, with few terminations more caudally in either the cervical enlargement or in the lumbosacral spinal segments. This finding suggests that PMv plays a role in the control of head movements. Furthermore, comparative neuroanatomical studies indicate that PMv's corticospinal projection was a primate innovation. If the first primates adapted to an arboreal life that involved unimanual feeding, as some experts believe, then perhaps PMv's corticospinal projection evolved to coordinate head movements with this kind of feeding behavior. The computations underlying such control could later be adapted to control head orientation during social signaling.

A previous study found that as monkeys learned novel mappings between visual cues and responses, neuronal activity patterns evolved at approximately the same time in both the dorsal premotor cortex (PMd) and the putamen. Here we report that, in both regions, the population activity for novel mappings came to resemble that for familiar ones as learning progressed. Both regions showed activity differences on trials with correct responses versus those with incorrect ones. In addition to these common features, we observed two noteworthy differences between PMd and putamen activity during learning. After a response choice had been made, but prior to feedback about the correctness of that choice (reward or nonreward), the putamen showed a sustained activity increase in activity, whereas PMd did not. Also in the putamen, this prereward activity was highly selective for the specific visuomotor mapping that had just been performed, and this selectivity was maintained until the time of the reward. After performance reached an asymptote, the degree of this selectivity decreased markedly to the level typical for familiar visuomotor mappings. These findings support the hypothesis that neurons in the striatum play a pivotal role in associative learning.

SummaryMany monkeys adopt abstract response strategies as they learn to map visual symbols to responses by trial and error. According to the repeat-stay strategy, if a symbol repeats from a previous, successful trial, the monkeys should stay with their most recent response choice. According to the change-shift strategy, if the symbol changes, the monkeys should shift to a different choice. We recorded the activity of prefrontal cortex neurons while monkeys chose responses according to these two strategies. Many neurons had activity selective for the strategy used. In a subsequent block of trials, the monkeys learned fixed stimulus-response mappings with the same stimuli. Some neurons had activity selective for choosing responses based on fixed mappings, others for choosing based on abstract strategies. These findings indicate that the prefrontal cortex contributes to the implementation of the abstract response strategies that monkeys use during trial-and-error learning.

To reinvestigate whether macaque monkeys could learn the reversed-contingency task, we trained six rhesus monkeys on the problem. On each trial, the monkeys chose between one and four pieces of the same food item. If a monkey selected four pieces of food, it received one instead; choice of one piece of food led to the receipt of four. All of the monkeys initially tended to select the larger quantity of food, but eventually learned to choose the smaller amount. The results confirmed a previous report that macaque monkeys quickly reached a performance level of roughly 50% ‘correct’, defined as choosing the smaller amount of food, and some individuals continued to perform at that level for a protracted period of testing. Contrary to that report, however, the present findings show that macaque monkeys can master the reversed-contingency task.

Conditional motor learning contributes importantly to behavioral flexibility. In previous work, the authors found that fornix transections impaired the ability of macaque monkeys (Macaca mulatta) to learn conditional motor associations between the nonspatial features of visual stimuli and nonspatially differentiated responses. In the present study, they found that significant 1-trial learning of such associations also depended on the fornix. Furthermore, removal of the hippocampus, subiculum, and subjacent parahippocampal cortex, added to fornix transection, had no effect, thus demonstrating that fornix transections eliminated the contribution of the hippocampal system. In addition, the authors examined the effect of errorless learning and found, in control monkeys, that errors made prior to the 1st correct response retarded 1-trial learning.

Historically, different groups of researchers have investigated the mechanisms of perceptual learning and motor learning. For sensory cortex, neurophysiological and psychophysical findings have linked changes in perception with altered neuronal tuning properties. However, less information has been forthcoming from motor cortex. This review compares recent findings on perceptual and motor learning, and suggests that similar mechanisms govern both. These mechanisms involve changes in both the center of neuronal tuning functions and their width or slope. The former reflects the values of the sensory or motor parameters that a neuron encodes, and the latter adjusts the encoding sensitivity. These similarities suggest that specific unifying principles for neural coding and computation exist across sensory and motor domains.