S. J. Gershman

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning

The paper shows that dopamine release in the nucleus accumbens during instrumental learning reflects Bayesian inference of probabilistic beliefs about hidden environmental states.

A probabilistic successor representation for context-dependent prediction

This paper introduces a probabilistic successor representation model that adapts to uncertainty and context switches, capturing animal behavior in contextual tasks and unifying SR theory with hippocampal function.

Undermatching is a consequence of policy compression

This paper explains undermatching in decision-making as a result of policy compression to minimize cognitive cost, and links it to dopamine levels in Parkinson's disease.

Rationally inattentive intertemporal choice

The paper reanalyzes data and conducts experiments to show that temporal discounting can arise from adaptive mental effort allocation due to simulation noise and rational inattention.

Inference and search on graph-structured spaces

The paper develops a Bayesian model of function learning on graph-structured spaces and shows it better predicts human behavior in reward search tasks than alternative models.

Origin of perseveration in the trade-off between reward and complexity

This study identifies perseveration as arising from a trade-off between reward maximization and policy complexity, showing human behavior aligns with theoretical predictions.

A theory of learning to infer

The paper proposes a theory explaining systematic deviations in human probabilistic inference by introducing a recognition model that adapts to query distribution.

Finding structure in multi-armed bandits

This study investigates how humans use latent structure in multi-armed bandit tasks to guide exploration and learning, finding that participants leverage functional relationships and develop learning-to-learn strategies.

Generalization as diffusion: human function learning on graphs

The paper tests a model of human function learning on graphs by having participants predict passenger volumes in a subway network, finding that diffusion-based generalization outperforms heuristic models.

Exploration in the wild

The paper analyzes customer behavior in an online food delivery service to understand exploratory strategies in real-world decision-making.

Reference-dependent preferences arise from structure learning

The paper shows that reference points in decision making are discovered through Bayesian structure learning, with changes in outcome distributions inducing new reference points and affecting risk preferences.

Decision by sampling implements efficient coding of psychoeconomic functions

This paper provides a normative justification for decision by sampling based on efficient coding principles, showing how it accounts for context-dependent decision-making.

Uncertainty and exploration

This study investigates how humans balance exploration and exploitation by manipulating total and relative uncertainty in a multi-armed bandit task, finding that both random and directed exploration strategies are independently used.

Compositional Inductive Biases in Function Learning

The paper shows humans prefer compositional structures in function learning, finding them more predictable and memorable than non-compositional ones.

Remembrance of Inferences Past

The paper shows humans use amortized inference in probabilistic reasoning, reusing summary statistics from previous queries to save cognitive resources.