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.

With multiple learning and memory systems at its disposal, the human brain can represent the past in many ways, from extracting regularities across similar experiences (incremental learning) to storing rich, idiosyncratic details of individual events (episodic memory). The unique information carried by these neurologically distinct forms of memory can bias our behavior in different directions, raising crucial questions about how these memory systems interact to guide choice and the factors that cause one to dominate. Here, we devised a new approach to estimate how decisions are independently influenced by episodic memories and incremental learning. Furthermore, we identified a biologically motivated factor that biases the use of different memory types—the detection of novelty versus familiarity. Consistent with computational models of cholinergic memory modulation, we find that choices are more influenced by episodic memories following the recognition of an unrelated familiar image but more influenced by incrementally learned values after the detection of a novel image. Together this work provides a new behavioral tool enabling the disambiguation of key memory behaviors thought to be supported by distinct neural systems while also identifying a theoretically important and broadly applicable manipulation to bias the arbitration between these two sources of memories.

Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals—estimated using two reinforcement learning models—tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.

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, PD ON ) or low (off medication, PD OFF ) dopaminergic states. Compared with both PD OFF and healthy controls, PD ON 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.

The hippocampus and the striatum are thought to play distinct roles in learning and memory, each supporting an independent memory system. A fundamental question is whether, and how, these systems interact to jointly contribute to learning and memory. In particular, it remains unknown whether the striatum contributes selectively to implicit, habitual learning, or whether the striatum may also contribute to long-term episodic memory. Here, we show with functional magnetic resonance imaging (fMRI) that the hippocampus and the striatum interact cooperatively to support episodic memory formation. Participants were scanned during a memory encoding paradigm and, subsequently, were tested for memory of encoded items. fMRI data revealed that successful memory was associated with greater activity in both the hippocampus and the striatum (putamen) during encoding. Furthermore, activity in the hippocampus and the striatum was correlated within subjects for items that were later remembered, but not for items that were forgotten. Finally, across subjects, the strength of the correlation between the hippocampus and the striatum predicted memory success. These findings provide novel evidence for contributions of both the striatum and the hippocampus to successful episodic encoding and for a cooperative interaction between them.

The hippocampus and the basal ganglia are thought to play fundamental and distinct roles in learning and memory, supporting two dissociable memory systems. Interestingly, however, the hippocampus and the basal ganglia have each, separately, been implicated as necessary for reversal learning—the ability to adaptively change a response when previously learned stimulus–outcome contingencies are reversed. Here, we compared the contribution of the hippocampus and the basal ganglia to distinct aspects of learning and reversal. Amnesic subjects with selective hippocampal damage, Parkinson subjects with disrupted basal ganglia function, and healthy controls were tested on a novel probabilistic learning and reversal paradigm. In this task, reversal can be achieved in two ways: Subjects can reverse a previously learned response, or they can select a new cue during the reversal phase, effectively “opting out” of the reversal. We found that both patient groups were intact at initial learning, but differed in their ability to reverse. Amnesic subjects failed to reverse, and continued to use the same cue and response learned before the reversal. Parkinson subjects, by contrast, opted out of the reversal by learning a new cue–outcome association. These results suggest that both the hippocampus and the basal ganglia support reversal learning, but in different ways. The basal ganglia are necessary for learning a new response when a previously learned response is no longer rewarding. The failure of the amnesic subjects to reverse their response or to learn a new cue is consistent with a more general role for the hippocampus in configural learning, and suggests it may also support the ability to respond to changes in cue–outcome contingencies.

Based on prior animal and computational models, we propose a double dissociation between the associative learning deficits observed in patients with medial temporal (hippocampal) damage versus patients with Parkinson's disease (basal ganglia dysfunction). Specifically, we expect that basal ganglia dysfunction may result in slowed learning, while individuals with hippocampal damage may learn at normal speed. However, when challenged with a transfer task where previously learned information is presented in novel recombinations, we expect that hippocampal damage will impair generalization but basal ganglia dysfunction will not. We tested this prediction in a group of healthy elderly with mild-to-moderate hippocampal atrophy, a group of patients with mild Parkinson's disease, and healthy controls, using an “acquired equivalence” associative learning task. As predicted, Parkinson's patients were slower on the initial learning but then transferred well, while the hippocampal atrophy group showed the opposite pattern: good initial learning with impaired transfer. To our knowledge, this is the first time that a single task has been used to demonstrate a double dissociation between the associative learning impairments caused by hippocampal versus basal ganglia damage/dysfunction. This finding has implications for understanding the distinct contributions of the medial temporal lobe and basal ganglia to learning and memory.