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Objective: Anorexia nervosa (AN) is characterized by severe restriction of calorie intake, which persists despite serious medical and psychological sequelae of starvation. Several prior studies have identified impaired feedback learning among individuals with AN, but whether it reflects a disturbance in learning from positive feedback (i.e., reward), negative feedback (i.e., punishment), or both, and the extent to which this impairment is related to severity and duration of illness, has not been clarified., Method: Participants were female adolescents with AN ( n = 76) and healthy teen volunteers (HC; n = 38) between the ages of 12-18 years who completed a probabilistic reinforcement learning task. A Bayesian reinforcement learning model was used to calculate separate learning rates for positive and negative feedback. Exploratory analyses examined associations between feedback learning and duration of illness, eating disorder severity, and self/parent reports of reward and punishment sensitivity., Results: Adolescents with AN had a significantly lower rate of learning from positive feedback relative to HC. Patients and HC did not differ in learning from negative feedback or on overall task performance measures. Feedback learning parameters were not significantly associated with duration of illness, eating disorder severity, or questionnaire-based reports of reward and punishment sensitivity., Conclusion: Adolescents with AN showed a circumscribed deficit in learning from reward that was not associated with duration of illness or reported sensitivity to reward or punishment. Subsequent longitudinal research should explore whether differences in learning from positive feedback relate to course of illness in youth with AN.

Background: Anorexia nervosa (AN) is a serious psychiatric illness that remains difficult to treat. Elucidating the neural mechanisms of AN is necessary to identify novel treatment targets and improve outcomes. A growing body of literature points to a role for dorsal fronto-striatal circuitry in the pathophysiology of AN, with increasing evidence of abnormal task-based fMRI activation within this network among patients with AN. Whether these abnormalities are present at rest and reflect fundamental differences in brain organization is unclear., Methods: The current study combined resting-state fMRI data from patients with AN ( n = 89) and healthy controls (HC; n = 92) across four studies, removing site effects using ComBat harmonization. First, the a priori hypothesis that dorsal fronto-striatal connectivity strength - specifically between the anterior caudate and dlPFC - differed between patients and HC was tested using seed-based functional connectivity analysis with small-volume correction. To assess specificity of effects, exploratory analyses examined anterior caudate whole-brain connectivity, amplitude of low-frequency fluctuations (ALFF), and node centrality., Results: Compared to HC, patients showed significantly reduced right, but not left, anterior caudate-dlPFC connectivity ( p = 0.002) in small-volume corrected analyses. Whole-brain analyses also identified reduced connectivity between the right anterior caudate and left superior frontal and middle frontal gyri ( p = 0.028) and increased connectivity between the right anterior caudate and right occipital cortex ( p = 0.038). No group differences were found in analyses of anterior caudate ALFF and node centrality., Conclusions: Decreased coupling of dorsal fronto-striatal regions indicates that circuit-based abnormalities persist at rest and suggests this network may be a potential treatment target.

Background: Restriction of food intake is a central pathological feature of anorexia nervosa (AN). Maladaptive eating behavior and, specifically, limited intake of calorie-dense foods are resistant to change and contribute to poor long-term outcomes. This study is a preliminary examination of whether change in food choices during inpatient treatment is related to longer-term clinical course., Methods: Individuals with AN completed a computerized Food Choice Task at the beginning and end of inpatient treatment to determine changes in high-fat and self-controlled food choices. Linear regression and longitudinal analyses tested whether change in task behavior predicted short-term outcome (body mass index [BMI] at discharge) and longer-term outcome (BMI and eating disorder psychopathology)., Results: Among 88 patients with AN, BMI improved significantly with hospital treatment ( p < 0.001), but Food Choice Task outcomes did not change significantly. Change in high-fat and self-controlled choices was not associated with BMI at discharge ( r = 0.13, p = 0.22 and r = 0.10, p = 0.39, respectively). An increase in the proportion of high-fat foods selected ( β = 0.91, p = 0.02) and a decrease in the use of self-control ( β = -1.50, p = 0.001) predicted less decline in BMI over 3 years after discharge., Conclusions: Short-term treatment is associated with improvement in BMI but with no significant change, on average, in choices made in a task known to predict actual eating. However, the degree to which individuals increased high-fat choices during treatment and decreased the use of self-control over food choice were associated with reduced weight loss over the following 3 years, underscoring the need to focus on changing eating behavior in treatment of AN.

Background: Restriction 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., Methods: This 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., Results: Behavioral 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., Conclusions: A 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.

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 used 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 women and women 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 STATEMENT An open question about the OFC is whether it supports the evaluation of food-related attributes during deliberation about what to eat. We found that healthiness and tastiness information was decodable from patterns of neural activity in the OFC in both patients with 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., (Copyright © 2022 the authors.)

Background: Anorexia nervosa is a severe illness with a high mortality rate, driven in large part by severe and persistent restriction of food intake. A critical challenge is to identify brain mechanisms associated with maladaptive eating behavior and whether they change with treatment. This study tested whether food choice-related caudate activation in anorexia nervosa changes with treatment., Methods: Healthy women (n = 29) and women hospitalized with anorexia nervosa (n = 24), ages 18 to 40 years, completed a Food Choice Task during fMRI scanning at two timepoints. Among patients, procedures occurred upon hospital admission (Time 1) and again after patients had gained to normal weight (Time 2). Healthy controls were tested twice at an interval group-matched to patients. Choice-related caudate activation was assessed at each timepoint, using parametric analyses in an a priori region of interest., Results: Among patients, the proportion of high-fat foods selected did not change over time (p's > 0.47), but decreased neural activity in the caudate after treatment was associated with increased selection of high-fat foods (r 23  = - 0.43, p = 0.037). Choice-related caudate activation differed among women with anorexia nervosa vs healthy control women at Time 1 (healthy control: M = 0.15 ± 0.87, anorexia nervosa: M = 0.70 ± 1.1, t 51  = - 2.05, p = 0.045), but not at Time 2 (healthy control: M = 0.18 ± 1.0, anorexia nervosa: M = 0.37 ± 0.99, t 51  = - 0.694, p = 0.49)., Conclusions: Caudate activity was more strongly associated with decisions about food among individuals with anorexia nervosa relative to healthy comparison individuals prior to treatment, and decreases in caudate engagement among individuals with anorexia nervosa undergoing treatment were associated with increases in high-fat food choices. The findings underscore the need for treatment development that more successfully alters both eating behavior and the neural mechanisms that guide it.

Research in computational psychiatry has sought to understand the basis of compulsive behavior by relating it to basic psychological and neural mechanisms: specifically, goal-directed versus habitual control. These psychological categories have been further identified with formal computational algorithms, model-based and model-free learning, which helps to provide quantitative tools to distinguish them. Computational psychiatry may be particularly useful for examining phenomena in individuals with anorexia nervosa (AN), whose self-starvation appears both excessively goal directed and habitual. However, these laboratory-based studies have not aimed to examine complex behavior, as seen outside the laboratory, in contexts that extend beyond monetary rewards. We therefore assessed (1) whether behavior in AN was characterized by enhanced or diminished model-based behavior, (2) the domain specificity of any abnormalities by comparing learning in a food-specific (i.e., illness-relevant) context as well as in a monetary context, and (3) whether impairments were secondary to starvation by comparing learning before and after initial treatment. Across all conditions, individuals with AN, relative to healthy controls, showed an impairment in model-based, but not model-free, learning, suggesting a general and persistent contribution of habitual over goal-directed control, across domains and time points. Thus, eating behavior in individuals with AN that appears very goal-directed may be under more habitual than goal-directed control, and this is not remediated by achieving weight restoration.

Food images are useful stimuli for the study of cognitive processes as well as eating behavior. To enhance rigor and reproducibility in task-based research, it is advantageous to have stimulus sets that are publicly available and well characterized. Food Folio by Columbia Center for Eating Disorders is a publicly available set of 138 images of Western food items. The set was developed for the study of eating disorders, particularly for use in tasks that capture eating behavior characteristic of these illnesses. It contains foods that are typically eaten, as well as those typically avoided, by individuals with eating disorders. Each image has now been rated across 17 different attributes by a large general United States population sample via Amazon's Mechanical Turk ( n = 1054). Ratings included subjective attributes (e.g., tastiness, healthiness, and favorable texture) as well as estimates of nutrient content (e.g., fat and carbohydrate). Each participant rated a subset of stimulus set food items (46 foods) on all 17 dimensions. Additional description of the image set is provided in terms of physical image information and accurate nutritional information. Correlations between subjective ratings were calculated and an exploratory factor analysis and exploratory cluster analysis completed. Outcomes of the factor analysis suggested foods may be described along three latent factors of healthiness, tastiness, and umami taste; the cluster analysis highlighted five distinct clusters of foods varying on these same dimensions. Descriptive outcomes indicated that the stimulus set includes a range of foods that vary along multiple dimensions and thus is likely to be useful in addressing various research questions surrounding eating behavior and cognition in healthy populations, as well as in those with eating disorders. The provision of comprehensive descriptive information allows for stimulus selection that is optimized for a given research question and promotes strong inference., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Lloyd, Shehzad, Schebendach, Bakkour, Xue, Assaf, Jilani, Walsh, Steinglass and Foerde.)

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., (© 2020 Wiley Periodicals LLC.)

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., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (© 2020 The Authors. International Journal of Eating Disorders published by Wiley Periodicals, Inc.)

Purpose of Review: This article reviews new research in the context of existing literature to identify approaches that will advance understanding of the persistence of anorexia nervosa., Recent Findings: 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., Summary: 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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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. 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.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Complex learned behaviors must involve the integrated action of distributed brain circuits. Although the contributions of individual regions to learning have been extensively investigated, much less is known about how distributed brain networks orchestrate their activity over the course of learning. 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 in humans. 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 ( n = 22; 13 females). Moreover, we found that this flexibility in striatal network coupling correlates with participants' learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that episodic learning, measured separately 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 STATEMENT Learning 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 updating values for actions or cues. Missing from this account, however, is an understanding of how 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., (Copyright © 2018 the authors 0270-6474/18/382442-12$15.00/0.)

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 (F 3,180  = 2.75, p = .048) and lower accuracy during the Test phase (F 1,60  = 4.29, p = .043), as compared with HC. Individuals with AN did not differ from HC in accuracy for novel stimuli (F 1,60  = 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., (© 2017 Wiley Periodicals, Inc.)

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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Choosing between smaller prompt rewards and larger later rewards is a common choice problem, and studies widely agree that frontostriatal circuits heavily innervated by dopamine are centrally involved. Understanding how dopamine modulates intertemporal choice has important implications for neurobiological models and for understanding the mechanisms underlying maladaptive decision-making. However, the specific role of dopamine in intertemporal decisions is not well understood. Dopamine may play a role in multiple aspects of intertemporal choices--the valuation of choice outcomes and sensitivity to reward delays. To assess the role of dopamine in intertemporal decisions, we tested Parkinson disease patients who suffer from dopamine depletion in the striatum, in either high (on medication, PDON) or low (off medication, PDOFF) dopaminergic states. Compared with both PDOFF and healthy controls, PDON made more farsighted choices and reduced their valuations less as a function of increasing time to reward. Furthermore, reduced discounting in the high dopaminergic state was robust across multiple measures, providing new evidence for dopamine's role in making decisions about the future.

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment., (© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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.

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., (© The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

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.)

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., (Copyright © 2012 S. Karger AG, Basel.)

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., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

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 neuropsychological 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.

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.

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.

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.