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Objective Individuals with anorexia nervosa (AN) pursue low-fat, low-calorie diets even when in a state of emaciation. These maladaptive food choices may involve fronto-limbic circuitry associated with cognitive control, habit, and reward. We assessed whether high-frequency repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (DLPFC) influenced food-related choice behavior in patients with severe, enduring (SE)-AN. Method Thirty-four females with SE-AN completed a Food Choice Task before and after 20 sessions of real or sham rTMS treatment and at a 4-month follow-up. During the task, participants rated high- and low-fat food items for healthiness and tastiness and then made a series of choices between a neutral-rated food and high- and low-fat foods. Outcomes included the proportion of high-fat and self-controlled choices made. A comparison group of 30 healthy women completed the task at baseline only. Results Baseline data were consistent with previous findings: relative to healthy controls, SE-AN participants showed a preference for low-fat foods and exercised self-control on a greater proportion of trials. There was no significant effect of rTMS treatment nor time on food choices related to fat content. However, among SE-AN participants who received real rTMS, there was a decrease in self-controlled food choices at post-treatment, relative to baseline. Specifically, there was an increase in the selection of tasty-unhealthy foods. Discussion In SE-AN, rTMS may promote more flexibility in relation to food choice. This may result from neuroplastic changes in the DLPFC and/or in associated brain areas.

Repetitive transcranial magnetic stimulation (rTMS) is used to modulate neural systems and provides the opportunity for experimental tests of hypotheses regarding mechanisms underlying anorexia nervosa (AN). The present pilot study has investigated whether high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) to a region of the right dorsolateral prefrontal cortex (DLPFC) might be associated with change in food selection among adult inpatients with AN. Ten women received one session of sham and one session of HF-rTMS targeting the right DLPFC while completing a computerized Food Choice Task. Compared to sham, HF-rTMS was associated with changes in food ratings and food choice: inpatients reported higher healthiness ratings of low- and high-fat foods and selected a significantly greater proportion of high-fat foods over a neutrally rated reference item while receiving HF-rTMS. Findings suggest that HF-rTMS to the right DLPFC was associated with a reduction of fat avoidance on a food choice task among inpatients with AN and provide additional support for the possibility that this region, and related neural circuits, may underlie restrictive food choice. Research using rTMS to experimentally test neural mechanisms is needed to elucidate the underpinnings of AN and supports the development of novel treatment targets.

BackgroundAnorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted MRI (dMRI) and tractography to reconstruct white matter pathways.MethodsdMRI data acquired from female patients with AN (n = 147) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes), and were compared between groups using multiple linear regression.ResultsIndividuals with AN, relative to HC, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC (p < 0.05, permutation corrected). Severity of illness, assessed by BMI, was associated with subcortical connectivity (p < 0.05, uncorrected).ConclusionsAnalyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, amongst individuals with AN relative to HC. These findings are consistent with alterations in feeding, emotion and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.

This article reviews new research in the context of existing literature to identify approaches that will advance understanding of the persistence of anorexia nervosa.Neuroscience research in anorexia nervosa has yielded disparate findings: no definitive neural mechanism underlying illness vulnerability or persistence has been identified and no clear neural target for intervention has emerged. Recent advances using structural and functional neuroimaging research, as well as new techniques for applying and combining these approaches, have led to a refined understanding of changes in neural architecture among individuals who are acutely ill, have undergone renourishment, or are in recovery/remission. In particular, advances have come from the incorporation of computational and translational approaches, as well as efforts to link experimental paradigms with illness-relevant behavior. Recent findings converge to suggest abnormalities in systems involved in reward learning and processing among individuals with anorexia nervosa.Anorexia nervosa is associated with neurobiological abnormalities. Aberrant learning and reward processing may contribute to the persistence of illness. To better utilize new techniques to understand the neural mechanisms of persistent anorexia nervosa, it may help to distinguish stages of illness and to link neurobiology with maladaptive behavior.

Objective Negative affect is a precipitant for binge eating in bulimia nervosa (BN). The purpose of the current study was to examine the effect of negative affect on food choices on a more granular level among individuals with BN using a computerized Food Choice Task. Method Individuals with BN (n = 25) and healthy controls (HC, n = 21) participated in a computerized Food Choice Task following negative and neutral affect inductions, across two study sessions. During the task participants rated high and low-fat food items for Healthiness and Tastiness. Individuals then made a series of choices between a neutral-rated food and high and low-fat foods and were then given a snack based upon these choices. Results Overall negative affect score increased significantly for both the BN and HC groups following the negative affect induction. The group of individuals with BN, relative to the HC group, was less likely to choose high-fat foods (z = −2.763, p = 0.006), and these choices were not impacted by affect condition. Health ratings influenced food choices significantly more among individuals with BN than HC (z = 2.55, p = 0.01). Discussion Induction of negative affect was successful, yet was not related to an increase in proportion of high-fat food choices in the group of individuals with BN. The Food Choice Task captured dietary restriction in individuals with BN and results highlight the utility of this task as a probe to examine how the values of healthiness and tastiness impact food choice in individuals with BN.

Decisions about what to eat recruit the orbitofrontal cortex (OFC) and involve the evaluation of food-related attributes, such as taste and health. These attributes are utilized differently by healthy individuals and patients with disordered eating behavior, but it is unclear whether these attributes are decodable from activity in the OFC in both groups and whether neural representations of these attributes are differentially related to decisions about food. We used fMRI combined with behavioral tasks to investigate the representation of taste and health attributes in the human OFC and the role of these representations in food choices in healthy individuals and patients with anorexia nervosa (AN). We found that subjective ratings of tastiness and healthiness could be decoded from patterns of activity in the OFC in both groups. However, health-related patterns of activity in the OFC were more related to the magnitude of choice preferences among patients with AN than healthy individuals. These findings suggest that maladaptive decision-making in AN is associated with more consideration of health information represented by the OFC during deliberation about what to eat.Significance StatementAn open question about the orbitofrontal cortex (OFC) is whether it supports the evaluation of food-related attributes during deliberation about what to eat. We found that healthiness and tastiness information were decodable from patterns of neural activity in the OFC in both patients with anorexia nervosa (AN) and healthy controls. Critically, neural representations of health were more strongly related to choices in patients with AN, suggesting that maladaptive overconsideration of healthiness during deliberation about what to eat is related to activity in the OFC. More broadly, these results show that activity in the human OFC is associated with the evaluation of relevant attributes during value-based decision-making. These findings may also guide future research into the development of treatments for AN.

BackgroundRestriction of food intake is a central feature of anorexia nervosa (AN) and other eating disorders, yet also occurs in the absence of psychopathology. The neural mechanisms of restrictive eating in health and disease are unclear.MethodsThis study examined behavioral and neural mechanisms associated with restrictive eating among individuals with and without eating disorders. Dietary restriction was examined in four groups of women (n = 110): healthy controls, dieting healthy controls, patients with subthreshold (non-low weight) AN, and patients with AN. A Food Choice Task was administered during fMRI scanning to examine neural activation associated with food choices, and a laboratory meal was conducted.ResultsBehavioral findings distinguished between healthy and ill participants. Healthy individuals, both dieting and non-dieting, chose significantly more high-fat foods than patients with AN or subthreshold AN. Among healthy individuals, choice was primarily influenced by tastiness, whereas, among both patient groups, healthiness played a larger role. Dorsal striatal activation associated with choice was most pronounced among individuals with AN and was significantly associated with selecting fewer high-fat choices in the task and lower caloric intake in the meal the following day.ConclusionsA continuous spectrum of behavior was suggested by the increasing amount of weight loss across groups. Yet, data from this Food Choice Task with fMRI suggest there is a behavioral distinction between illness and health, and that the neural mechanisms underlying food choice in AN are distinct. These behavioral and neural mechanisms of restrictive eating may be useful targets for treatment development.

What are the neural substrates of habit learning in humans? Studies in neuropsychological populations have been central to answering the question, and for decades, research appeared to have provided a fairly consistent answer. However, developments in assays of habits in animals, as well as new approaches to dissecting habitual versus goal-directed control of behavior in humans, point to further complexities in human habit learning. This has raised new questions about the status of habits in neuropsychological populations and our understanding of how the brain supports habitual behavior. I review these emerging challenges and suggest a more nuanced approach to habit learning.

Objective Anorexia Nervosa (AN) is a serious disorder, with a mortality rate the highest of any psychiatric illness. It is notoriously challenging to treat and mechanisms of illness are not well understood. Reward system abnormalities have been proposed across theoretical models of the persistence of AN. Feedback learning is an important component of how reward systems shape behavior and we hypothesized that individuals with AN would show poorer learning from feedback. Methods We administered the acquired equivalence task to measure both learning from incremental feedback and generalization of that learning to novel stimuli. Participants were individuals with AN (n = 36) before and after intensive weight restoration treatment and healthy comparison participants (HC, n = 26) tested twice. Performance was assessed as accuracy during the Learning and Test phases, for both trained and novel stimuli. The relationship between task performance and eating disorder severity at baseline was also assessed. Results Both before and after treatment, individuals with AN showed reduced learning from feedback in the Learning phase (F3,180 = 2.75, p = .048) and lower accuracy during the Test phase (F1,60 = 4.29, p = .043), as compared with HC. Individuals with AN did not differ from HC in accuracy for novel stimuli (F1,60 = 1.04, p = .312), indicating no deficit in generalization. Decreased acquisition of feedback learning was associated with longer illness duration and with greater eating disorder symptom severity at baseline. Conclusions Individuals with AN show reduced learning from feedback or reinforcement, which may contribute to difficulties in changing maladaptive behaviors.

Objective By definition, restricting (ANR) and binge-eating/purging (ANBP) subtypes of anorexia nervosa (AN) differ in some manifestations of maladaptive eating behavior. This study aimed to determine whether the groups differ in the choices they make about what to eat, and whether there are differences in valuation related to food choice, using an experimental paradigm. Method Inpatients with ANR (n = 40) and ANBP (n = 46) participated in a Food Choice Task. During the task, participants rated 76 food images for healthiness and tastiness, and choice preferences. Groups were compared in percent selection of high-fat and low-fat foods, value ratings of foods, and engagement of self-control in food choice. Results There were no differences between AN subtypes in healthiness or tastiness ratings, or in tendency to limit choice of high-fat foods. There was no difference between the groups in measures of self-control in food choice. Discussion Individuals with ANR and ANBP similarly manifest reduced choices of high-fat foods, with similar tendencies to undervalue the tastiness of high-fat foods. These results suggest that while individuals with ANR and ANBP differ across a range of clinical characteristics, the decision-making process associated with the maladaptive restriction of high-fat foods characteristic of AN is shared by both subtypes.

Patients with Parkinson’s disease have reduced reward sensitivity related to dopaminergic neuron loss, which is associated with impairments in reinforcement learning. Increasingly, however, dopamine-dependent reward signals are recognized to play an important role beyond reinforcement learning. In particular, it has been shown that reward signals mediated by dopamine help guide the prioritization of events for long-term memory consolidation. Meanwhile, studies of memory in patients with Parkinson’s disease have focused on overall memory capacity rather than what is versus what isn’t remembered, leaving open questions about the effect of dopamine replacement on the prioritization of memories by reward and the time-dependence of this effect. The current study sought to fill this gap by testing the effect of reward and dopamine on memory in patients with Parkinson’s disease. We tested the effect of dopamine modulation and reward on two forms of long-term memory: episodic memory for neutral objects and memory for stimulus-value associations. We measured both forms of memory in a single task, adapting a standard task of reinforcement learning with incidental episodic encoding events of trial-unique objects. Objects were presented on each trial at the time of feedback, which was either rewarding or not. Memory for the trial-unique images and for the stimulus-value associations, and the influence of reward on both, was tested immediately after learning and 2 days later. We measured performance in Parkinson’s disease patients tested either ON or OFF their dopaminergic medications and in healthy older control subjects. We found that dopamine was associated with a selective enhancement of memory for reward-associated images, but that it did not influence overall memory capacity. Contrary to predictions, this effect did not differ between the immediate and delayed memory tests. We also found that while dopamine had an effect on reward-modulated episodic memory, there was no effect of dopamine on memory for stimulus-value associations. Our results suggest that impaired prioritization of cognitive resource allocation may contribute to the early cognitive deficits of Parkinson’s disease.

PURPOSE OF REVIEW: The persistent maladaptive eating behavior characteristic of anorexia nervosa (AN) can be understood as a learned habit. This review describes the cognitive neuroscience background and the existing data from research in AN. RECENT FINDINGS: Behavior is habitual after it is frequently repeated and becomes nearly automatic, relatively insensitive to outcome, and mediated by dorsal frontostriatal neural systems. There is evidence for such behavior in AN, in which restrictive intake has been related to dorsal frontostriatal systems. Other neural and neurocognitive data provide mixed findings, some of which suggest disturbances in habit systems in AN. SUMMARY: There are compelling behavioral and neural data to suggest that habit systems may underlie the persistence of AN. The habit model needs further research, via more direct behavioral hypothesis testing and probes of the development of habitual behavior. Investigation of the habit-centered model of AN may open avenues for the development of novel treatments.

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants’ learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.Significance StatementLearning from the outcomes of actions–referred to as reinforcement learning–is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

Aberrations in eating patterns constitute a substantial public health burden. Computer-based paradigms that measure responses to images of foods are potentially useful tools for assessing food attitudes and characteristics of eating behavior. In particular, food choice tasks attempt to directly probe aspects of individuals' decisions about what to eat. In the Food Choice Task participants rate the healthiness and tastiness of a variety of food items presented one at a time. Next, participants choose for each food item whether they prefer to eat the item vs. a neutrally rated reference food item. The goal of the current study was to assess the stability and reliability of this Food Choice Task over time and with repeated testing. Secondary analyses were conducted using data from healthy volunteers in two separate studies that administered the task at two time points, separated either by several days or about a month. The overall reliability of the Food Choice Task across multiple administrations was assessed using intra-class correlation coefficients and the reliability of ratings of individual food items was assessed using kappa coefficients. The results indicated that test-retest reliability of the Food Choice Task in healthy volunteers was high at both shorter and longer test-retest intervals. In addition, the reliability of individual food item ratings was good for a majority of items. The proportion of healthy volunteers’ high-fat food choices did not change over time in either of the two studies. Thus, the Food Choice Task is suitable for measuring food choices in studies with multiple assessment points. In particular, the task may be well suited to assess restrictive eating, a construct which it has been difficult to assess in experimental settings.

Learning and motivation are intrinsically related, and both have been linked to dopamine. Parkinson's disease results from a progressive loss of dopaminergic inputs to the striatum and leads to impairments in motivation and learning from feedback. However, the link between motivation and learning in Parkinson's disease is not well understood. To address this gap, we leverage a well-established psychological theory of motivation, regulatory mode theory, which distinguishes between two functionally independent motivational concerns in regulating behavior: a concern with having an effect by initiating and maintaining movement (Locomotion) and a concern with establishing what is correct by critically evaluating goal pursuit means and outcomes (Assessment). We examined Locomotion and Assessment in patients with Parkinson's disease and age-matched controls. Parkinson's disease patients demonstrated a selective decrease in Assessment motivation but no change in Locomotion motivation, suggesting that Parkinson's disease leads to a reduced tendency to evaluate and monitor outcomes. Moreover, weaker Assessment motivation was correlated with poorer performance on a feedback-based learning task previously shown to depend on the striatum. Together, these findings link a questionnaire-based personality inventory with performance on a well-characterized experimental task, advancing our understanding of how Parkinson's disease affects motivation with implications for well-being and treatment outcomes.

Objective Inadequate intake and preference for low-calorie foods are salient behavioral features of Anorexia Nervosa (AN). The neurocognitive mechanisms underlying pathological food choice have not been characterized. This study aimed to develop a new paradigm for experimentally modeling maladaptive food choice in AN. Method Individuals with AN (n = 22) and healthy controls (HC, n = 20) participated in a computer-based Food Choice Task, adapted for individuals with eating disorders. Participants first rated 43 food images (including high-fat and low-fat items) for Healthiness and Tastiness; an item rated neutral on both blocks was then selected as the Reference item. On each of 42 subsequent trials participants were asked to choose between the food item presented and the Reference item. Results The AN group was less likely to choose high-fat foods relative to HC, as evidenced both in multilevel logistic regression (z = 2.59, p = .009) and ANOVA (F(1,39) = 7.80, p = .008) analyses. Health ratings influenced choice significantly more in AN relative to HC (z = 2.7, p = .006), and were more related to Taste among AN (χ[2] = 4.10, p = .04). Additionally, taste ratings declined with duration of illness (r = −.50, p = .02). Discussion The Food Choice Task captures the preference for low-fat foods among individuals with AN. The findings suggest that the experience of tastiness changes over time and may contribute to perpetuation of illness. By providing an experimental quantitative measure of food restriction, this task opens the door to new experimental investigations into the cognitive, affective, and neural factors contributing to maladaptive food choices characteristic of AN. © 2014 Wiley Periodicals, Inc. (Int J Eat Disord 2015; 48:59–66)

People routinely make poor choices, despite knowledge of negative consequences. The authors found that individuals with anorexia nervosa, who make maladaptive food choices to the point of starvation, engaged the dorsal striatum more than healthy controls when making choices about what to eat, and that activity in fronto-striatal circuits was correlated with their actual food consumption in a meal the next day.

Parkinson’s disease (PD) is characterized by a loss of dopaminergic projections to the striatum, leading to both motor and cognitive impairments. The cognitive impairments are relatively selective and include deficits in incremental learning from trial-by-trial feedback, while other forms of learning, such as hippocampal-dependent episodic memory, remain intact. Interestingly, it has been suggested that the striatum and the hippocampus compete during learning, leading to the intriguing prediction that the striatal disruption in PD could lead to enhanced performance on tasks that depend on the hippocampus. We tested this prediction by simultaneously assessing incremental learning and episodic memory for trial-unique feedback events, within a single task, in patients with PD. Further, in order to modulate the engagement of the striatum versus the hippocampus, we manipulated the timing of feedback during learning, building on prior results showing that delaying feedback by a few seconds shifts learning to depend on the hippocampus instead of the striatum. We found that Parkinson’s patients were impaired at learning from immediate feedback, but had enhanced episodic memory for those immediate feedback events. Thus, our results provide evidence for concurrent impaired and enhanced learning and memory functions within the same group of patients from a single task.

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinson's disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning.

Nondeclarative learning refers to abilities characterized by a lack of awareness of what has been learned and an independence from medial temporal lobe structures that support conscious memories of facts and events. Neuroimaging approaches have been used extensively in two domains of nondeclarative learning—priming and skill learning—to investigate the neural substrates supporting performance. Recent neuroimaging studies have attempted to understand what is being learned in different tasks in order to inform psychological theories of nondeclarative memory. For example, priming may be considered a form of perceptual learning or a form of stimulus–response learning, and correlations between performance and activation patterns in different regions may suggest the nature of the brain changes that support behavior. The attainment of expertise in a skill has been characterized as greater efficiency of processing in the same neural structures that support novice performance or, alternatively, as the recruitment of additional regions. Current research suggests that, within the domains of priming and skill learning, there is much heterogeneity in the underlying brain representations and psychological theories will need to account for these variations.

Probabilistic classification learning can be supported by implicit knowledge of cue-response associations. We investigated whether forming these associations depends on attention by assessing the effect of performing a secondary task on learning in the probabilistic classification task (PCT). Experiment I showed that concurrent task performance significantly interfered with performance of the PCT. Experiment 2 showed that this interference did not prevent learning from occurring. On the other hand, the secondary task did disrupt acquisition of explicit knowledge about cue-outcome associations. These results show that concurrent task performance can have different effects on implicit and explicit knowledge acquired within the same task and also underscore the importance of considering effects on learning and performance separately.

Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brain's reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, we found that adolescents showed better reinforcement learning and a stronger link between reinforcement learning and episodic memory for rewarding outcomes. This behavioral benefit was related to heightened prediction error-related BOLD activity in the hippocampus and to stronger functional connectivity between the hippocampus and the striatum at the time of reinforcement. These findings reveal an important role for the hippocampus in reinforcement learning in adolescence and suggest that reward sensitivity in adolescence is related to adaptive differences in how adolescents learn from experience.

Different forms of learning and memory depend on functionally and anatomically separable neural circuits [Squire, L. R. (1992) Psychol. Rev. 99, 195–231]. Declarative memory relies on a medial temporal lobe system, whereas habit learning relies on the striatum [Cohen, N. J. & Eichenbaum, H. (1993) Memory, Amnesia, and the Hippocampal System (MIT Press, Cambridge, MA)]. How these systems are engaged to optimize learning and behavior is not clear. Here, we present results from functional neuroimaging showing that the presence of a demanding secondary task during learning modulates the degree to which subjects solve a problem using either declarative memory or habit learning. Dual-task conditions did not reduce accuracy but reduced the amount of declarative learning about the task. Medial temporal lobe activity was correlated with task performance and declarative knowledge after learning under single-task conditions, whereas performance was correlated with striatal activity after dual-task learning conditions. These results demonstrate a fundamental difference in these memory systems in their sensitivity to concurrent distraction. The results are consistent with the notion that declarative and habit learning compete to mediate task performance, and they suggest that the presence of distraction can bias this competition. These results have implications for learning in multitask situations, suggesting that, even if distraction does not decrease the overall level of learning, it can result in the acquisition of knowledge that can be applied less flexibly in new situations.

Acquisition of a new skill is generally associated with a decrease in the need for effortful control over performance, leading to the development of automaticity. Automaticity by definition has been achieved when performance of a primary task is minimally affected by other ongoing tasks. The neural basis of automaticity was examined by testing subjects in a serial reaction time (SRT) task under both single-task and dual-task conditions. The diminishing cost of dual-task performance was used as an index for automaticity. Subjects performed the SRT task during two functional magnetic imaging sessions separated by 3 h of behavioral training over multiple days. Behavioral data showed that, by the end of testing, subjects had automated performance of the SRT task. Before behavioral training, performance of the SRT task concurrently with the secondary task elicited activation in a wide network of frontal and striatal regions, as well as parietal lobe. After extensive behavioral training, dual-task performance showed comparatively less activity in bilateral ventral premotor regions, right middle frontal gyrus, and right caudate body; activity in other prefrontal and striatal regions decreased equally for single-task and dual-task conditions. These data suggest that lateral and dorsolateral prefrontal regions, and their corresponding striatal targets, subserve the executive processes involved in novice dual-task performance. The results also showed that supplementary motor area and putamen/globus pallidus regions showed training-related decreases for sequence conditions but not for random conditions, confirming the role of these regions in the representation of learned motor sequences.

The ability to learn from feedback is a key component of adaptive behavior. This type of learning is traditionally thought to depend on neural substrates in the striatum and not on the medial temporal lobe (MTL). Here we show that in humans the MTL becomes necessary for feedback-based learning when feedback is delayed. Specifically, amnesic patients with MTL damage were impaired at probabilistic learning of cue–outcome associations when response-contingent feedback was delayed by a few seconds, but not when feedback was immediate. By contrast, patients with striatal dysfunction due to Parkinson's disease demonstrated the opposite pattern: impaired learning when trial-by-trial feedback was immediate but not when feedback was delayed, indicating that the striatum is necessary for learning only when feedback is immediate. Together, these results reveal that multiple complementary learning processes support what appears to be identical behavior in healthy individuals and point to an important role for the MTL in feedback-driven learning.

The ability to learn from the consequences of actions—no matter when those consequences take place—is central to adaptive behavior. Despite major advances in understanding how immediate feedback drives learning, it remains unknown precisely how the brain learns from delayed feedback. Here, we present converging evidence from neuropsychology and neuroimaging for distinct roles for the striatum and the hippocampus in learning, depending on whether feedback is immediate or delayed. We show that individuals with striatal dysfunction due to Parkinson's disease are impaired at learning when feedback is immediate, but not when feedback is delayed by a few seconds. Using functional imaging (fMRI) combined with computational model-derived analyses, we further demonstrate that healthy individuals show activation in the striatum during learning from immediate feedback and activation in the hippocampus during learning from delayed feedback. Additionally, later episodic memory for delayed feedback events was enhanced, suggesting that engaging distinct neural systems during learning had consequences for the representation of what was learned. Together, these findings provide direct evidence from humans that striatal systems are necessary for learning from immediate feedback and that delaying feedback leads to a shift in learning from the striatum to the hippocampus. The results provide a link between learning impairments in Parkinson's disease and evidence from single-unit recordings demonstrating that the timing of reinforcement modulates activity of midbrain dopamine neurons. Collectively, these findings indicate that relatively small changes in the circumstances under which information is learned can shift learning from one brain system to another.

Procedural learning is a form of nondeclarative memory, which, in contrast to declarative memory, does not rely on conscious memory for how learning occurred. These distinct forms of memory were discovered as intact ability to learn new skills in the face of dense amnesia following damage to the medial temporal lobe. Procedural learning encompasses a wide range of motor, perceptual, and cognitive skills and is commonly measured as improved task performance. Different types of procedural learning involve varying cortical regions, depending on the task domain, whereas the cerebellum and in particular the striatum play general roles in acquisition and expression of learning.

It has been suggested that patients with schizophrenia have corticostriatal circuit dysfunction (Carlsson & Carlsson, 1990). Skill learning is thought to rely on corticostriatal circuitry and different types of skill learning may be related to separable corticostriatal loops (Grafton, Hazeltine, & Ivry, 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999). The authors examined motor (Serial Reaction Time task, SRT) and cognitive (Probabilistic Classification task, PCT) skill learning in patients with schizophrenia and normal controls. Development of automaticity was examined, using a dual task paradigm, across three training sessions. Patients with schizophrenia were impaired at learning on the PCT compared to controls. Performance gains of controls occurred within the first session, whereas patients only improved gradually and never reached the performance level of controls. In contrast, patients were not impaired at learning on the SRT relative to controls, suggesting that patients with schizophrenia may have dysfunction in a specific corticostriatal subcircuit.

A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuro psycho logical studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience. (c) 2007 Elsevier Ltd. All rights reserved.