World's Best Scientists 2026 revealed!
Samuel J. Gershman

Samuel J. Gershman

D-Index & Metrics

Computer Science

D-Index
74
Citations
24832
World Ranking
1477
National Ranking
769

Psychology

D-Index
72
Citations
23506
World Ranking
2040
National Ranking
1189

Research.com Recognitions

  • 2018 - Fellow of Alfred P. Sloan Foundation

Overview

Samuel J. Gershman is affiliated with Harvard University in the United States. Their research spans multiple fields with a primary focus on neuroscience and computer science. Gershman's work addresses key areas within cognitive neuroscience, artificial intelligence, cellular and molecular neuroscience, developmental and educational psychology, and general decision sciences.

The scientist has contributed extensively to various research topics including:

  • Neural dynamics and brain function
  • Neural and behavioral psychology studies
  • Memory and neural mechanisms
  • Decision-making and behavioral economics
  • Child and animal learning development
  • Reinforcement learning in robotics
  • Receptor mechanisms and signaling

Gershman's frequent publication venues demonstrate a broad engagement with both preprint and peer-reviewed outlets, including:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • PLoS Computational Biology
  • Cognition
  • Nature Human Behaviour

Among their published papers are several notable works from 2020 and 2021, such as:

  • A Unified Framework for Dopamine Signals across Timescales (2020, Cell)
  • Hippocampal remapping as hidden state inference (2020, eLife)
  • Structured Event Memory: A neuro-symbolic model of event cognition (2020, Psychological Review)
  • Resource-rational decision making (2021, Current Opinion in Behavioral Sciences)
  • Dissociable neural correlates of uncertainty underlie different exploration strategies (2020, Nature Communications)

Frequent collaborators in Gershman's work include:

  • Joshua B. Tenenbaum
  • Eric Schulz
  • Momchil S. Tomov
  • Yang Xiang
  • Lucy Lai

In addition to articles, Gershman has authored books published by Princeton University Press, notably What Makes Us Smart (2021).

Recognition for their academic contributions includes receiving the fellowship from the Alfred P. Sloan Foundation awarded in 2018.

Best Publications

  • Building machines that learn and think like people.

    Brenden M. Lake;Tomer David Ullman;Joshua B Tenenbaum;Samuel J Gershman

  • Model-based influences on humans' choices and striatal prediction errors.

    Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan

  • The hippocampus as a predictive map

    Kimberly L Stachenfeld;Matthew M Botvinick;Samuel J Gershman

  • Computational rationality: A converging paradigm for intelligence in brains, minds, and machines

    Samuel J. Gershman;Eric J. Horvitz;Joshua B. Tenenbaum

  • A Tutorial on Bayesian Nonparametric Models

    Samuel J. Gershman;David M. Blei

  • Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.

    Samuel J Gershman;Nathaniel D Daw

  • The successor representation in human reinforcement learning.

    I. Momennejad;E. M. Russek;J. H. Cheong;M. M. Botvinick

  • Context, learning, and extinction.

    Samuel J. Gershman;David M. Blei;Yael Niv

  • Reinforcement learning in multidimensional environments relies on attention mechanisms

    Yael Niv;Reka Daniel;Andra Geana;Samuel J. Gershman

  • Predictive representations can link model-based reinforcement learning to model-free mechanisms.

    Evan M. Russek;Ida Momennejad;Matthew M. Botvinick;Samuel J. Gershman

  • The Curse of Planning Dissecting Multiple Reinforcement-Learning Systems by Taxing the Central Executive

    A. Ross Otto;Samuel J. Gershman;Arthur B. Markman;Nathaniel D. Daw

  • Learning latent structure: Carving nature at its joints

    Samuel J Gershman;Yael Niv

  • Accountability of AI Under the Law: The Role of Explanation

    Finale Doshi-Velez;Mason Kortz;Ryan Budish;Christopher Bavitz

  • Toward a universal decoder of linguistic meaning from brain activation

    Francisco Pereira;Bin Lou;Brianna Pritchett;Samuel Ritter

  • A Unified Framework for Dopamine Signals across Timescales

    HyungGoo R. Kim;Athar N. Malik;John G. Mikhael;Pol Bech

  • Retrospective revaluation in sequential decision making: a tale of two systems.

    Samuel J. Gershman;Arthur B. Markman;A. Ross Otto

  • Interplay of approximate planning strategies

    Quentin J. M. Huys;Quentin J. M. Huys;Níall Lally;Níall Lally;Paul Faulkner;Neir Eshel

  • Deconstructing the human algorithms for exploration.

    Samuel J. Gershman

  • Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

    Wouter Kool;Samuel J Gershman;Fiery A Cushman

  • Amortized Inference in Probabilistic Reasoning

    Samuel Gershman;Noah D. Goodman

Frequent Co-Authors

Yael Niv
Yael Niv Princeton University
Kenneth A. Norman
Kenneth A. Norman Princeton University
Geoffrey Schoenbaum
Geoffrey Schoenbaum National Institutes of Health
Matthew Botvinick
Matthew Botvinick Yale University
Fiery Cushman
Fiery Cushman Harvard University
David M. Blei
David M. Blei Columbia University
Nathaniel D. Daw
Nathaniel D. Daw Princeton University
Noah D. Goodman
Noah D. Goodman Stanford University
Marie H. Monfils
Marie H. Monfils The University of Texas at Austin

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