Biology subjects

Shohamy, D.

Publications and source records attributed to Shohamy, D..

16 recordsLinked to original sources

bioRxiv2026

Tasting from memory: reconstructing taste from visual cues in the human brain

Taste guides us toward nourishment and away from harm. Yet when we decide what to eat, taste only exists in memory, evoked by a cue. How the brain retrieves taste memories is almost entirely unknown. If memory re-creates experience, it should reproduce the brain's activation while tasting, even in the absence of taste. We scanned participants while they formed and then retrieved associations between tastes and visual cues. The gustatory cortex was engaged during retrieval and distinguished among remembered tastes, but not using the patterns it had formed while tasting. Patterns persisted instead in lateral parietal cortex, distinguishing individual tastes, not just taste-categories. Activity in the amygdala covaried with this same parietal region and amygdala activity predicted which associations were retained. Taste memory is not simply a re-creation of sensory experience, but a distinct representation, outside the taste system, yet specific enough to guide what we choose to eat.

neuroscience↗

bioRxiv2026

Neural representations supporting generalization under continual learning

Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations--which are typically made after learning has converged--demonstrate the product of abstraction, but not the process by which abstract knowledge is learned: how are the inputs from concrete experiences transformed into abstract knowledge, and how do neural circuits perform these operations and relay this knowledge? To address these questions, we developed a factorized model of temporal abstraction that builds on the successor representation. The model disentangles the contributions of different levels of abstract learning--from stimulus-stimulus associations to a generalizable task schema--in the form of a factorized prediction error that relates the change in relational knowledge to a predicted change in representational geometry on each trial. We fit the model to the behavior of human participants performing a context-dependent decision task during fMRI. The model captured the learning dynamics at multiple timescales, including the increasing contribution of generalization as participants transferred abstracted relational knowledge between novel task instances. In fMRI, BOLD activity in hippocampus--where, in past work, abstract knowledge was represented after learning--was increasingly attributed to the acquisition of abstract knowledge based on generalization. A similar temporal pattern was observed in entorhinal cortex, a putative source of low-dimensional structural information, and orbitofrontal cortex (OFC), which may depend on relational knowledge to represent state relationships as a cognitive map that guides choices. Indeed, individual variation in the generalization signal in OFC correlated with behavioral performance on key trials that required relational knowledge. Our findings show how the brain regions previously shown to represent abstract knowledge after learning also support the process of abstraction as it evolves from learning concrete associations to a generalizable schema. Our approach offers a computational framework for disentangling the operations driving abstract learning and probing their neural correlates in the dynamics of representational geometry.

neuroscience↗

bioRxiv2024

Two routes to value-based decisions in Parkinsons disease: differentiating incremental reinforcement learning from episodic memory

Patients with Parkinsons disease are impaired at incremental reward-based learning. It is typically assumed that this impairment reflects a loss of striatal dopamine. However, many open questions remain about the nature of reward-based learning deficits in Parkinsons. Recent studies have found that a combination of different cognitive and computational strategies contribute even to simple reward-based learning tasks, suggesting a possible role for episodic memory. These findings raise critical questions about how incremental learning and episodic memory interact to support learning from past experience and what their relative contributions are to impaired decision-making in Parkinsons disease. Here we addressed these questions by asking patients with Parkinsons disease (n=26) both on and off their dopamine replacement medication and age- and education-matched healthy controls (n=26) to complete a task designed to isolate the contributions of incremental learning and episodic memory to reward-based learning and decision-making. We found that Parkinsons patients performed as well as healthy controls when using episodic memory, but were impaired at incremental reward-based learning. Dopamine replacement medication remediated this deficit while enhancing subsequent episodic memory for the value of motivationally relevant stimuli. These results demonstrate that Parkinsons patients are impaired at learning about reward from trial-and-error when episodic memory is properly controlled for, and that learning based on the value of single experiences remains intact in patients with Parkinsons disease.

neuroscience↗

bioRxiv2024

Value construction through sequential sampling explains serial dependencies in decision making

Many decisions are expressed as a preference for one item over another. When these items are familiar, it is often assumed that the decision maker assigns a value to each of the items and chooses the item with the highest value. These values may be imperfectly recalled, but are assumed to be stable over the course of an interview or psychological experiment. Choices that are inconsistent with a stated valuation are thought to occur because of unspecified noise that corrupts the neural representation of value. Assuming that the noise is uncorrelated over time, the pattern of choices and response times in value-based decisions are modeled within the framework of Bounded Evidence Accumulation (BEA), similar to that used in perceptual decision-making. In BEA, noisy evidence samples accumulate over time until the accumulated evidence for one of the options reaches a threshold. Here, we argue that the assumption of temporally uncorrelated noise, while reasonable for perceptual decisions, is not reasonable for value-based decisions. Subjective values depend on the internal state of the decision maker, including their desires, needs, priorities, attentional state, and goals. These internal states may change over time, or undergo revaluation, as will the subjective values. We reasoned that these hypothetical value changes should be detectable in the pattern of choices made over a sequence of decisions. We reanalyzed data from a well-studied task in which participants were presented with pairs of snacks and asked to choose the one they preferred. Using a novel algorithm (Reval), we show that the subjective value of the items changes significantly during a short experimental session (about 1 hour). Values derived with Reval explain choice and response time better than explicitly stated values. They also better explain the BOLD signal in the ventromedial prefrontal cortex, known to represent the value of decision alternatives. Revaluation is also observed in a BEA model in which successive evidence samples are not assumed to be independent. We argue that revaluation is a consequence of the process by which values are constructed during deliberation to resolve preference choices.

neuroscience↗

bioRxiv2023

Proactive and reactive construction of memory-based preferences

We are often faced with decisions we have never encountered before, requiring us to infer possible outcomes before making a choice. Computational theories suggest that one way to make these types of decisions is by accessing and linking related experiences stored in memory. Past work has shown that such memory-based preference construction can occur at a number of different timepoints relative to the moment a decision is made. Some studies have found that memories are integrated at the time a decision is faced (reactively) while others found that memory integration happens earlier, when memories were initially encoded (proactively). Here we offer a resolution to this inconsistency, demonstrating that these two strategies tradeoff rationally as a function of the associative structure of memory. We use fMRI to decode patterns of brain responses unique to categories of images in memory and find that proactive memory access is more common and allows more efficient inference. However, we also find that participants use reactive access when choice options are linked to a larger number of memory associations. Together, these results indicate that the brain judiciously conducts proactive inference by accessing memories ahead of time when conditions make this strategy more favorable.

neuroscience↗

bioRxiv2022

The role of the cerebellum in learning to predict reward: evidence from cerebellar ataxia

Recent findings in animals have challenged the traditional view of the cerebellum solely as the site of motor control, suggesting that the cerebellum may also be important for learning to predict reward from trial-and-error feedback. Yet, evidence for the role of the cerebellum in reward learning in humans is lacking. Moreover, open questions remain about which specific aspects of reward learning the cerebellum may contribute to. Here we address this gap through an investigation of multiple forms of reward learning in individuals with cerebellum dysfunction, represented by cerebellar ataxia cases. Nineteen participants with cerebellar ataxia and 57 age- and sex-matched healthy controls completed two separate tasks that required learning about reward contingencies from trial-and-error. To probe the selectivity of reward learning processes, the tasks differed in their underlying structure: while one task measured incremental reward learning ability alone, the other allowed participants to use an alternative learning strategy based on episodic memory alongside incremental reward learning. We found that individuals with cerebellar ataxia were profoundly impaired at reward learning from trial-and-error feedback on both tasks, but retained the ability to learn to predict reward based on episodic memory. These findings provide evidence from humans for a specific and necessary role for the cerebellum in incremental learning of reward associations based on reinforcement. More broadly, the findings suggest that alongside its role in motor learning, the cerebellum likely operates in concert with the basal ganglia to support reinforcement learning from reward.

neuroscience↗

bioRxiv2022

Uncertainty alters the balance between incremental learning and episodic memory

A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

neuroscience↗

bioRxiv2021

Signatures of visuospatial information foraging during learning of complex environments

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them, a process distinct from learning about near-term reward contingencies. Prior efforts to study latent feature learning often use single decisions, use few features, and fail to distinguish between reward-seeking and informationseeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. Reward and information outcomes from uncovering parts of shapes were not perfectly correlated and their effects could be disentangled. Members of both species adeptly learned the shapes and preferred to select informative tiles earlier in trials than rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

animal behavior and cognition↗

bioRxiv2021

Neural Representations of Food-Related Attributes in the Human Orbitofrontal Cortex in Health and Disease

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.

neuroscience↗

bioRxiv2021

Preserved motor memory in Parkinson's disease

Patients with Parkinsons disease, who lose the dopaminergic projections to the striatum, are impaired in certain aspects of motor learning. Recent evidence suggests that, in addition to its role in motor performance, the striatum plays a key role in the memory of motor learning. Whether Parkinsons patients have impaired motor memory and whether motor memory is modulated by dopamine at the time of initial learning is unknown. To address these questions, we measured memory of a learned motor sequence in Parkinsons patients who were either On or Off their dopaminergic medications. We compared them to a group of older and younger controls. Contrary to our predictions, motor memory was not impaired in patients compared to older controls, and was not influenced by dopamine state at the time of initial learning. To probe post-learning consolidation processes, we also tested whether learning a new sequence shortly after learning the initial sequence would interfere with later memory. We found that, in contrast to younger adults, neither older adults nor patients were susceptible to this interference. These findings suggest that motor memory is preserved in Parkinsons patients and raise the possibility that motor memory in patients is supported by compensatory non-dopamine sensitive mechanisms. Furthermore, given the similar performance characteristics observed in the patients and older adults and the absence of an effect of dopamine, these results raise the possibility that aging and Parkinsons disease affect motor memory in similar ways.

neuroscience↗

bioRxiv2020

Racial and ethnic imbalance in neuroscience reference lists and intersections with gender

Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.

neuroscience↗

bioRxiv2019

Memory for Individual Items is Related to Non-Reinforced Preference Change

It is commonly assumed that memories contribute to value-based decisions. Nevertheless, most theories of value-based decision-making do not account for memory influences on choice. Recently, new interest has emerged in the interactions between these two fundamental processes, mainly using reinforcement-based paradigms. Here, we aimed to study the role memory processes play in preference change following the non-reinforced cue-approach training (CAT) paradigm. In CAT, the mere association of cued items with a speeded motor response influences choices. Previous studies with this paradigm showed that a single training session induces a long-lasting effect of enhanced preferences for high-value trained stimuli, that is maintained for several months. We hypothesized that CAT influences memory accessibility for trained items, leading to enhanced accessibility of their positive associative memories and in turn to preference changes. In two pre-registered experiments, we tested whether memory for trained items was enhanced following CAT, in the short and in the long-term, and whether memory modifications were related to choices. We found that memory was enhanced for trained items and that better memory was correlated with enhanced preferences at the individual item level, both immediately and one month following CAT. Our findings show that memory plays a central role in value-based decision-making following CAT, even in the absence of external reinforcements. These findings contribute to new theories relating memory and value-based decision-making and set the groundwork for the implementation of novel behavioral interventions that lead to long-lasting behavioral change.

neuroscience↗

bioRxiv2019

Dopamine modulates learning-related changes in dynamic striatal-cortical connectivity in Parkinson’s disease

Learning from reinforcement is thought to depend on striatal dopamine inputs, which serve to update the value of actions by modifying connections in widespread cortico-striatal circuits. While considerable research has described the activity of individual striatal and midbrain regions in reinforcement learning, the broader role for dopamine in modulating network-level processes has been difficult to decipher. To examine whether dopamine modulates circuit-level dynamic connectivity during learning, we characterized the effects of dopamine on learning-related dynamic functional connectivity estimated from fMRI data acquired in patients with Parkinsons disease. Patients with Parkinsons disease have severe dopamine depletion in the striatum and are treated with dopamine replacement drugs, providing an opportunity to compare learning and network dynamics when patients are in a low dopamine state (off drugs) versus a high dopamine state (on drugs). We assessed the relationship between dopamine and dynamic connectivity while patients performed a probabilistic reversal learning task. We found that reversal learning altered dynamic network flexibility in the striatum and that this effect was dependent on dopaminergic state. We also found that dopamine modulated changes in connectivity between the striatum and specific task-relevant visual areas of inferior temporal cortex, providing empirical support for theories stipulating that value is updated through changes in cortico-striatal circuits. These results suggest that dopamine exerts a widespread effect on neural circuitry and network dynamics during reinforcement learning.

neuroscience↗

bioRxiv2018

Two Sides of the Same Coin: The Hippocampus as a Common Neural Substrate for Model-Based Planning and Spatial Memory

Little is known about the neural mechanisms that allow humans and animals to plan actions using knowledge of task contingencies. Emerging theories hypothesize that it involves the same hippocampal mechanisms that support self-localization and memory for locations. Yet, there is limited direct evidence for the link between model-based planning and the hippocampal place map. We addressed this by investigating model-based planning and place memory in healthy controls and epilepsy patients treated using unilateral anterior temporal lobectomy with hippocampal resection. We found that both functions were impaired in the patient group. Specifically, the planning impairment was related to right hippocampal lesion size, controlling for overall lesion size. Furthermore, planning and place memory covaried with one another, but only in neurologically intact controls, consistent with both functions relying on the same structure in the healthy brain. These findings clarify the scope of hippocampal contributions to behavior and the neural mechanism of model-based planning.

neuroscience↗

bioRxiv2018

Value-based decisions involve sequential sampling from memory

Deciding between two equally appealing options can take considerable time. This observation has puzzled economists and philosophers, because more deliberation only delays the reward. Here we show that this seemingly irrational behavior is explained by the constructive use of memory. Using functional brain imaging in humans, we show that how long it takes to decide between two familiar food items is related to activity in the hippocampus, within specific regions shown to be associated with the retrieval of long-term memories. Moreover, we show that value is partially constructed during deliberation to resolve preference, and this constructive process changes behavior and brain responses. These results render memory as a supplier of evidence in value-based decisions, resolving a central paradox of choice.

animal behavior and cognition↗

bioRxiv2016

Dynamic flexibility in striatal-cortical circuits supports reinforcement learning

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.\n\nSignificance 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.

neuroscience↗