Samuel Gershman

Samuel Gershman
Samuel
Gershman
Investigator
Harvard University
Department:  Department of Psychology Center for Brain Science

Associated Research Module: 

Associated Research Thrust: 

Email:  gershman@fas.harvard.edu
Faculty Profile
Website: 
Lab Website:  Computational Cognitive Neuroscience Lab

Current Advisees

Ruairidh McLennan Battleday - Postdoc
Alicia Chen - Research Assistant
Lucy Lai - Graduate Student
Jenna Landy - Summer Research Intern
Thomas Pouncy - Graduate Student
Felix Sosa - Graduate Student
Natalia Velez - Postdoc

Past Advisees

Ishita Dasgupta - Graduate Student
Wouter Kool - Postdoctoral Fellow
Prashant Raju - Research Assistant
Honi Sanders - Postdoc
Eric Schulz - Postdoc
Momchil Tomov - Postdoc

Projects

Competition and cooperation between multiple learning systems
Context-Dependent Processing in Rodent Hippocampus
Hierarchical motion perception
Stochastic hypothesis generation and mental simulation

CBMM Publications

C. Yu, Burgess, N., Sahani, M., and Gershman, S. J., “Successor-Predecessor Intrinsic Exploration”, in Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023.
Y. Li, Wang, Y. Q., Boger, T., Smith, K. A., Gershman, S. J., and Ullman, T., “An approximate representation of objects underlies physical reasoning”, psyArXiv, 2022.
T. Pouncy, Tsividis, P., and Gershman, S. J., “What Is the Model in Model‐Based Planning?”, Cognitive Science, vol. 45, no. 1, 2021.
M. S. Tomov, Schulz, E., and Gershman, S. J., “Multi-task reinforcement learning in humans”, Nature Human Behaviour, 2021.
S. Yang, Bill, J., Drugowitsch, J., and Gershman, S. J., “Human visual motion perception shows hallmarks of Bayesian structural inference”, Scientific Reports, vol. 11, no. 1, 2021.
I. Dasgupta and Gershman, S. J., “Memory as a Computational Resource”, Trends in Cognitive Sciences, vol. 25, no. 3, pp. 240 - 251, 2021.
D. McNamee, Stachenfeld, K., Botvinick, M. M., and Gershman, S. J., “Flexible modulation of sequence generation in the entorhinal-hippocampal system”, Nature Neuroscience, 2021.
F. A. Sosa, Ullman, T., Tenenbaum, J. B., Gershman, S. J., and Gerstenberg, T., “Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology”, Cognition, vol. 217, p. 104890, 2021.
Y. Xiang, Graeber, T., Enke, B., and Gershman, S. J., “Confidence and central tendency in perceptual judgment”, Attention, Perception, & Psychophysics, vol. 83, no. 7, pp. 3024 - 3034, 2021.
I. Dasgupta, Schulz, E., Tenenbaum, J. B., and Gershman, S. J., “A theory of learning to infer.”, Psychological Review, vol. 127, no. 3, pp. 412 - 441, 2020.
H. Sanders, Wilson, M. A., and Gershman, S. J., “Hippocampal remapping as hidden state inference”, eLife, vol. 9, 2020.
E. Schulz, Quiroga, F., and Gershman, S. J., “Communicating Compositional Patterns”, Open Mind, vol. 4, pp. 25 - 39, 2020.
J. Bill, Pailian, H., Gershman, S. J., and Drugowitsch, J., “Hierarchical structure is employed by humans during visual motion perception”, Proceedings of the National Academy of Sciences, vol. 117, no. 39, pp. 24581 - 24589, 2020.
S. J. Gershman, “Origin of perseveration in the trade-off between reward and complexity”, Cognition, vol. 204, p. 104394, 2020.
I. Dasgupta, Guo, D., Gershman, S. J., and Goodman, N. D., “Analyzing Machine‐Learned Representations: A Natural Language Case Study”, Cognitive Science, vol. 44, no. 12, 2020.
S. Liu, Cushman, F. A., Gershman, S. J., Kool, W., and Spelke, E. S., “Hard choices: Children’s understanding of the cost of action selection. ”, in Cognitive Science Society, 2019.
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “The transdiagnostic structure of mental effort avoidance”, Scientific Reports, vol. 9, no. 1, 2019.
S. J. Gershman, “How to never be wrong”, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs”, Biological Psychiatry, vol. 85, no. 5, pp. 425 - 433, 2019.
H. Sanders, Wilson, M. A., and Gershman, S. J., “Hippocampal Remapping as Hidden State Inference”. 2019.
S. J. Gershman, “The Generative Adversarial Brain”, Frontiers in Artificial Intelligence, vol. 2, 2019.
W. Kool, Gershman, S. J., and Cushman, F. A., “Planning Complexity Registers as a Cost in Metacontrol”, Journal of Cognitive Neuroscience, vol. 30, no. 10, pp. 1391 - 1404, 2018.
W. Kool, Gershman, S. J., and Cushman, F. A., “Thinking fast or slow? A reinforcement-learning approach”, Society for Personality and Social Psychology. San Antonio, TX, 2017.
P. Tsividis, Pouncy, T., Xu, J. L., Tenenbaum, J. B., and Gershman, S. J., “Human Learning in Atari”, in AAAI Spring Symposium Series, 2017.
S. J. Gershman and Daw, N. D., “Reinforcement learning and episodic memory in humans and animals: an integrative framework”, Annual Review of Psychology, vol. 68, 2017.
E. Schulz, Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M., and Gershman, S. J., “Compositional inductive biases in function learning.”, Cogn Psychol, vol. 99, pp. 44-79, 2017.
B. M. Lake, Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people.”, Behavioral and Brain Sciences, vol. 40, p. e253, 2017.
W. Kool, Gershman, S. J., and Cushman, F. A., “Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.”, Psychol Sci, vol. 28, no. 9, pp. 1321-1333, 2017.
B. M. Lake, Ullman, T., Tenenbaum, J. B., and Gershman, S. J., “Building machines that learn and think like people”. 2016.
E. Schulz, Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M., and Gershman, S. J., “Probing the compositionality of intuitive functions”. 2016.
I. Dasgupta, Schulz, E., and Gershman, S. J., “Where do hypotheses come from?”. 2016.
W. Kool, Cushman, F. A., and Gershman, S. J., “When Does Model-Based Control Pay Off?”, PLoS Comput Biol, vol. 12, no. 8, p. e1005090, 2016.
P. Tsividis, Gershman, S. J., Tenenbaum, J. B., and Schulz, L., “Information Selection in Noisy Environments with Large Action Spaces”, 9th Biennial Conference of the Cognitive Development Society, vol. Columbus, OH. 2015.
S. J. Gershman, Tenenbaum, J. B., and Jaekel, F., “Discovering hierarchical motion structure”, Vision Research, vol. Available online 26 March 2015, 2015.
S. J. Gershman, Horvitz, E. J., and Tenenbaum, J. B., “Computational rationality: A converging paradigm for intelligence in brains, minds, and machines”, Science, vol. 349, no. 6245, pp. 273-278, 2015.