Working papers
Lai, L., Huang, A.Z., & Gershman, S.J. (submitted). Action chunking as conditional policy compression.
Gershman, S.J., & Lak, A. (submitted). Policy complexity suppresses dopamine responses.
Velez, N., Wu, C.M., Gershman, S.J., & Schulz, E. (submitted). The rise and fall of technological development in virtual communities.
Battleday, R.M., & Gershman, S.J. (submitted). Artificial intelligence for science: the easy and hard problems.
Prat-Carrabin, A., & Gershman, S.J. (submitted). Bayes vs. Weber: how to break a law of psychophysics.
Gershman, S.J. (submitted). Rate estimation revisited. [code]
Hall-McMaster, S., Tomov, M., Gershman, S.J., & Schuck, N.W. (submitted). Neural prioritisation of past solutions supports generalisation.
Liu, S., Lai, L., Gershman, S.J., & Bari, B.A. (submitted). Time and memory costs jointly determine a speed-accuracy trade-off and set-size effects.
Bari, B.A., & Gershman, S.J. (submitted). The value of non-instrumental information in anxiety: insights from a resource-rational model of planning.
Bari, B.A., Krystal, A.D., Pizzagalli, D.A., & Gershman, S.J. (submitted). Computationally-informed insights into anhedonia and treatment by κ-opioid receptor antagonism.
Xiang, Y., Landy, J., Cushman, F.A., Velez, N., & Gershman, S.J. (submitted). People reward others based on their willingness to exert effort. [code+data]
Qian, L., Burrell, M., Hennig, J.A., Matias, S., Murthy, V.N., Gershman, S.J., Uchida, N. (submitted). The role of prospective contingency in the control of behavior and dopamine signals during associative learning.
Fry, B.R., Russell, N., Fex, V., Mo, B., Pence, N., Beatty, J.A., Manfreddsson, F.P., Toth, B.A., Burgess, C.R., Gershman, S.J., & Johnson, A.W. (submitted). Devaluing memories of reward: A case for dopamine.
Sosa, F., Gershman, S.J., & Ullman, T. (submitted). Blending simulation and abstraction for physical reasoning.
Binz et al. (submitted). How should the advent of large language models affect the practice of science?
Qu, A.J., Tai, L-H., Hall, C.D., Tu, E.M., Eckstein, M.K., Mischanchuk, K., Lin, W.C., Chase, J.B., MacAskill, A.F., Collins, A.G.E., Gershman, S.J., & Wilbrecht, L. (submitted). Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.
Orchinik, R., Dubey, R., Gershman, S.J., Powell, D., Bhui, R. (submitted). Learning from and about climate scientists.
Tsividis, P.A., Loula, J., Burga, J., Foss, N., Campero, A., Pouncy, T., Gershman, S.J., & Tenenbaum, J.B. (submitted). Human-level reinforcement learning through theory-based modeling, exploration, and planning.
In Press
Bezdek, M., Nguyen, T., Gershman, S.J., Bobick, A.F., Braver, T., & Zacks, J.M. (in press). Error-based updating of event representations enables prediction of human activity at human scale. PNAS Nexus.
Masset, P., & Gershman, S.J. (in press). Reinforcement learning with dopamine: a convergence of natural and artificial intelligence. In S. Cragg & M. Walton (eds). The Handbook of Dopamine. Elsevier.
Allen, K., Brändle, F., ... Schulz, E. (in press) Using games to understand the mind. Nature Human Behaviour.
2024
Carvalho, W., Tomov, M.S., de Cothi, W., Barry, C., & Gershman, S.J. (2024). Predictive representations: building blocks of intelligence. Neural Computation, 36, 2225-2298.
Bari, B.A., & Gershman, S.J. (2024). Resource-rational psychopathology. Behavioral Neuroscience, 138, 221-234.
Gershman, S.J., Assad, J.A., Datta, S.R., Linderman, S.W., Sabatini, B.L., Uchida, N., & Wilbrecht, L. (2024). Explaining dopamine through prediction errors and beyond. Nature Neuroscience, 27, 1645-1655.
Gershman, S.J. (2024). Habituation as optimal filtering. iScience, 27, 110523. [code]
Lu, Q., Nguyen, T.T., Zhang, Q., Hasson, U., Griffiths, T.L., Zacks, J.M., Gershman, S.J., & Norman, K.A. (2024). Reconciling shared versus context-specific information in a neural network model of latent causes. Scientific Reports, 14, 16782.
Chen, A.M., Palacci, A., Velez, N., Hawkins, R., & Gershman, S.J. (2024). A hierarchical Bayesian approach to adaptive teaching. Cognitive Science, 48, e13477. [code+data]
Schurr, R., Reznik, D., Hillman, H., Bhui, R., & Gershman, S.J. (2024). Dynamic computational phenotyping of human cognition. Nature Human Behaviour, 8, 917-931. [code+data]
Gershman, S.J. (2024). What have we learned about artificial intelligence from studying the brain? Biological Cybernetics, 118, 1-5.
Lai, L., & Gershman, S.J. (2024). Human decision making balances reward maximization and policy compression. PLOS Computational Biology, 20, e1012057. [code+data]
Beukers, A.O., Collin, S.H.P., Kempner, R.P., Franklin, N.T., Gershman, S.J., & Norman, K.A. (2024). Blocked training facilitates learning of multiple schemas. Communications Psychology, 2, 28. [code+data]
Geerts, J.P., Gershman, S.J., Burgess, N., & Stachenfeld, K.L. (2024). A probabilistic successor representation for context-dependent prediction. Psychological Review, 131, 578-597. [code]
Xiang, Y., Velez, N., & Gershman, S.J. (2024). Optimizing competence in the service of collaboration. Cognitive Psychology, 150, 101653. [code+data]
Kumar, T., Bordelon, B., Gershman, S.J., & Pehlevan, C. (2024). Grokking as the transition from lazy to rich training dynamics. International Conference on Learning Representations 12.
Bates, C.J., Alvarez, G., & Gershman, S.J. (2024). Scaling models of visual working memory to natural images. Communications Psychology, 2, 3. [code+data]
Zhao, J., Radke, J., Chen, F.S., Sachdeva, S., Gershman, S.J., & Luo, Y. (2024). How do we reinforce climate action? Sustainability Science, 19, 1503-1517.
Coppersmith, D.L., Jaroszewski, A.C., Gershman, S.J., Cha, C.B., Millner, A.J., Fortgang, R.G., Kleiman, E.M., & Nock, M.K. (2024). Do people know how suicidal they will be? Understanding suicidal prospection. Suicide and Life Threatening Behavior, 54, 750-761.
Gershman, S.J. (2024). The rational analysis of memory. In M. Kahana & A. Wagner (Eds.) Oxford Handbook of Human Memory. Oxford University Press.
2023
Yu, C., Burgess, N., Sahani, M., & Gershman, S.J. (2023). Successor-predecessor intrinsic exploration. Advances in Neural Information Processing Systems 36.
Li, Y., Wang, Y., Boger, T., Smith, K., Gershman, S.J., & Ullman, T.D. (2023). An approximate representation of objects underlies physical reasoning. Journal of Experimental Psychology: General, 152, 3074-3086. [code+data]
Hennig, J., Romeiro Pinto, S.A., Yamaguchi, T., Linderman, S.W., Uchida, N., & Gershman, S.J. (2023). Emergence of belief-like representations through reinforcement learning. PLOS Computational Biology, 19, e1011067. [code]
Brändle, F., Stocks, L.J., Tenenbaum, J.B., Gershman, S.J., & Schulz, E. (2023). Empowerment contributes to exploration behaviour in a creative video game. Nature Human Behaviour, 7, 1481-1489. [code+data]
Gershman, S.J., & Cikara, M. (2023). Structure learning principles of stereotype change. Psychonomic Bulletin & Review, 30, 1273-1293. [code]
Fan, H., Burke, T., Sambrano, D., Dial, E., Phelps, E.A., & Gershman, S.J. (2023). Pupil size encodes uncertainty during exploration. Journal of Cognitive Neuroscience, 35, 1508-1520. [code] [data]
Gershman, S.J., & Burke, T. (2023). Mental control of uncertainty. Cognitive, Affective, and Behavioral Neuroscience, 23, 465-475. [code+data]
Jakob, A., & Gershman, S.J. (2023). Rate-distortion theory of neural coding and its implications for working memory. eLife, 12, e79450. [code]
Biderman, N., Gershman, S.J., Shohamy, D. (2023). The role of memory in counterfactual valuation. Journal of Experimental Psychology: General, 152, 1754-1767. [code+data]
Xiang, Y., Landy, J., Cushman, F.A., Velez, N., & Gershman, S.J. (2023). Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks. Cognition, 241, 105609. [code+data]
Xiang, Y., Velez, N., & Gershman, S.J. (2023). Collaborative decision making is grounded in representations of other people's competence and effort. Journal of Experimental Psychology: General, 152, 1565-1579. [code+data]
Velez, N., Chen, A.M., Burke, T., Cushman, F.A., & Gershman, S.J. (2023). Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences, 120, e2215015120. [code+data]
Gershman, S.J., & Ullman, T.D. (2023). Causal implicatures from correlational statements. PLOS One, 18, e0286067. [data]
Tomov, M.S., Tsividis, P.A., Pouncy, T., Tenenbaum, J.B., & Gershman, S.J. (2023). The neural architecture of theory-based reinforcement learning. Neuron, 111, 1331-1344.
Fan, H., Gershman, S.J., & Phelps, E.A. (2023). Trait somatic anxiety is associated with reduced directed exploration and underestimation of uncertainty. Nature Human Behaviour, 7, 102-113.
Bari, B.A., & Gershman, S.J. (2023). Undermatching is a consequence of policy compression. Journal of Neuroscience, 43, 447-457. [code+data]
Gershman, S.J. (2023). The molecular memory code and synaptic plasticity: a synthesis. BioSystems, 224, 104825.
Orchinik, R., Dubey, R., Gershman, S.J., Powell, D., Bhui, R. (2023). Learning about scientists from climate consensus messaging. Proceedings of the 45th Annual Cognitive Science Society.
2022
McNamee, D., Stachenfeld, K.L., Botvinick, M.M., Gershman, S.J. (2022). Compositional sequence generation in the entorhinal-hippocampal system. Entropy, 24, 1791.
Bill, J., Gershman, S.J., & Drugowitsch, J. (2022). Visual motion perception as online hierarchical inference. Nature Communications, 13, 7403. [code]
Binz, M., Gershman, S.J., Schulz, E., & Endres, D. (2022). Heuristics from bounded meta-learned inference. Psychological Review, 129, 1042-1077.
Jakob, A.M.V., Mikhael, J.G., Hamilos, A.E., Assad, J.A., & Gershman, S.J. (2022). Dopamine mediates the bidirectional update of interval timing. Behavioral Neuroscience, 136, 445-452. [code+data]
Pouncy, T., & Gershman, S.J. (2022). Inductive biases in theory-based reinforcement learning. Cognitive Psychology, 138, 101509.
Alexander, W.H., & Gershman, S.J. (2022). Representation learning with reward prediction errors. Neurons, Behavior, Data Analysis, and Theory.
Mikhael, J.G., Kim, H.R., Uchida, N., & Gershman, S.J. (2022). The role of state uncertainty in the dynamics of dopamine. Current Biology, 32, 1077-1087.
Le, T.A., Collins, K.M., Hewitt, L., Ellis, K., Siddharth, N., Gershman, S.J., & Tenenbaum, J.B. (2022). Hybrid memoised wake-sleep: approximate inference at the discrete-continuous interface. 10th International Conference on Learning Representations.
Mikhael, J.G., & Gershman, S.J. (2022). Impulsivity and risk-seeking as Bayesian inference under dopaminergic control. Neuropsychopharmacology, 47, 465-476.
Bates, C.J., & Gershman, S.J. (2022). Coding strategies in memory for 3D objects: the influence of task uncertainty. Proceedings of the 44th Annual Conference of the Cognitive Science Society, 1077-1087.
2021
Gershman, S.J. (2021). What Makes Us Smart: The Computational Logic of Human Cognition. Princeton University Press. Princeton: NJ.
Xiang, Y., Graeber, T., Enke, B., & Gershman, S.J. (2021). Confidence and central tendency in perceptual judgment. Attention, Perception, & Psychophysics, 83. 3024-3034. [code+data]
Sosa, F.A., Ullman, T., Tenenbaum, J.B., Gershman, S.J., & Gerstenberg, T. (2021). Moral dynamics: grounding moral judgment in intuitive theories. Cognition, 217, 104890. [code+data]
Dorfman, H.M., Tomov, M., Cheung, B., Clarke, D., Gershman, S.J., & Hughes, B.L. (2021). Causal inference gates corticostriatal learning. Journal of Neuroscience, 41, 6892-6904.
Gershman, S.J. (2021). Just looking: the innocent eye in neuroscience. Neuron, 109, 2220-2223.
Lai, L., & Gershman, S.J. (2021). Policy compression: an information bottleneck in action selection. Psychology of Learning and Motivation, 74, 195-232.
Tomov, M., Schulz, E., & Gershman, S.J. (2021). Multi-task reinforcement learning in humans. Nature Human Behaviour, 5, 764-773. [code+data]
McNamee, D., Stachenfeld, K.L., Botvinick, M.M., Gershman, S.J. (2021). Flexible modulation of sequence generation in the entorhinal–hippocampal system. Nature Neuroscience, 24, 851-862. [supplement]
Gershman, S.J., & Lai, L. (2021). The reward-complexity trade-off in schizophrenia. Computational Psychiatry, 5, 38-53.
Wu, C.M., Schulz, E., & Gershman, S.J. (2021). Inference and search on graph-structured spaces. Computational Brain and Behavior, 4, 125-147. [code+data]
Wang, R., Mao, J., Gershman, S.J., & Wu, J. (2021). Language-mediated, object-centric representation learning. Findings of the Association for Computational Linguistics.
Mikhael, J.G., Lai, L., & Gershman, S.J. (2021). Rational inattention and tonic dopamine. PLOS Computational Biology, 17, e1008659. [code]
Bhui, R., Lai, L., & Gershman, S.J. (2021). Resource-rational decision making. Current Opinion in Behavioral Sciences, 41, 15-21.
Gershman, S.J., Guitart-Masip, M., & Cavanagh, J.F. (2021). Neural signatures of arbitration between Pavlovian and instrumental action selection. PLOS Computational Biology, 17, e1008553.
Dasgupta, I., & Gershman, S.J. (2021). Memory as a computational resource. Trends in Cognitive Sciences, 25, 240-251.
Yang, S., Bill, J., Drugowitsch, J., & Gershman, S.J. (2021). Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports, 11, 3714. [code+data]
Gershman, S.J., Balbi, P.E.M., Gallistel, C.R., & Gunawardena, J. (2021). Reconsidering the evidence for learning in single cells. eLife, 10, e61907.
Pouncy, T., Tsividis, P., & Gershman, S.J. (2021). What is the model in model-based planning? Cognitive Science, 45, e12928. [Supplementary materials]
Bhattasali, N.X., Tomov, M.S., & Gershman, S.J. (2021). CCNLab: A benchmarking framework for computational cognitive neuroscience. 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks.
Russek, E.M., Momennejad, I., Botvinick M.M., Gershman, S.J., & Daw, N.D. (2021). Neural evidence for the successor representation in choice evaluation.
2020
Kim, H.R., Malik, A.N., Mikhael, J.G., Bech, P., Tsutsui-Kimura, I., Sun, F., Zhang, Y., Li, Y., Watabe-Uchida, M., Gershman, S.J., & Uchida, N. (2020). A unified framework for dopamine signals across timescales. Cell, 183, 1600-1616.
Dasgupta, I., Guo, D., Gershman, S.J., Goodman, N.D. (2020). Analyzing machine-learned representations: A natural language case study. Cognitive Science, e12925.
Bhui, R., & Gershman, S.J. (2020). Paradoxical effects of persuasive messages. Decision, 7, 239-258.
Bill, J. Pailian, H., Gershman, S.J., & Drugowitsch, J. (2020). Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences, 117, 24581-24589. [Supplementary information] [Code and data]
Cohen, A.O., Nussenbaum, K., Dorfman, H.M., Gershman, S.J., & Hartley, C.A. (2020). The rational use of causal inference to guide reinforcement learning changes with age. NPJ Science of Learning, 5, 16.
Gershman, S.J. & Cikara, M. (2020). Social-structure learning. Current Directions in Psychological Science, 29, 460-466.
Gershman, S.J. (2020). Origin of perseveration in the trade-off between reward and complexity. Cognition, 204, 104394.
Schulz, E., Quiroga, F., & Gershman, S.J. (2020). Communicating compositional patterns. Open Mind, 4, 25-39.
Goldwater, M.B., Gershman, S.J., Moul, C., Ludowici, C., Burton, A., Killer, B., Kuhnert, R-L., Ridgway, K. (2020). Children's understanding of habitual behaviour. Developmental Science, 23, e12951.
Gershman, S.J. & Bhui, R. (2020). Rationally inattentive intertemporal choice. Nature Communications, 11, 3365.
Baumann, C., Singmann, H., Gershman, S.J., & von Helversen, B. (20). A linear threshold model for optimal stopping behavior. Proceedings of the National Academy of Sciences, 117, 12750-12755.
Sanders, H., Wilson, M.A., Gershman, S.J. (2020). Hippocampal remapping as hidden state inference. eLife, 9, e51140.
Gershman, S.J., & Olveczky, B.P. (2020). The neurobiology of deep reinforcement learning. Current Biology, 30, R617-R634.
Tomov, M., Truong, V., Hundia, R., & Gershman, S.J. (2020). Dissociable neural correlates of uncertainty underlie different exploration strategies. Nature Communications, 11, 2371.
Tomov, M., Yagati, S., Kumar, A., Yang, W., & Gershman, S.J. (2020). Discovery of hierarchical representations for efficient planning. PLOS Computational Biology, 16, e1007594.
Lau, T., Gershman, S.J., & Cikara, M. (2020). Social structure learning in human anterior insula. eLife, e53162.
Dasgupta, I., Schulz, E., Tenenbaum, J.B. & Gershman, S.J. (2020). A theory of learning to infer. Psychological Review, 127, 412-441.
Franklin, N.T., Norman, K.A., Ranganath, C., Zacks, J.M., & Gershman, S.J. (2020). Structured event memory: a neuro-symbolic model of event cognition. Psychological Review, 127, 327-361.
Schulz, E., Franklin, N.T. & Gershman, S.J. (2020). Finding structure in multi-armed bandits. Cognitive Psychology, 119, 101261. [code+data]
Kleiman-Weiner, M., Sosa, F., Thompson, B., Opheusden, B., Griffiths, T.L., Gershman, S.J., & Cushman, F. (2020). Downloading culture.zip: social learning by program induction. Proceedings of the 42nd Annual Conference of the Cognitive Science Society.
2019
Dorfman, H.M., & Gershman, S.J. (2019). Controllability governs the balance between Pavlovian and instrumental action selection. Nature Communications, 10, 5826.
Stalnaker, T., Howard, J., Takahashi, Y., Gershman, S.J., Kahnt, T., & Schoenbaum, G. (2019). Dopamine neuron ensembles signal the content of sensory prediction errors. eLife, 8, e49315.
Gershman, S.J. (2019). What does the free energy principle tell us about the brain? Neurons, Behavior, Data Analysis, and Theory.
Gershman, S.J. & Uchida, N. (2019). Believing in dopamine. Nature Reviews Neuroscience, 20, 703-714.
Schulz, E., Bhui, R., Love, B.C., Brier, B., Todd, M.T., & Gershman, S.J. (2019). Structured, uncertainty-driven exploration in real-world consumer choice. Proceedings of the National Academy of Sciences, 116, 13903-13908.
Gershman, S.J. (2019). The generative adversarial brain. Frontiers in Artificial Intelligence, 2, 18.
Chang, L.W., Gershman, S.J., & Cikara, M. (2019). Comparing value coding models of context-dependence in social choice. Journal of Experimental Social Psychology, 85, 103847.
Gershman, S.J. (2019). Uncertainty and exploration. Decision, 6, 277-286.
Mikhael, J.G. & Gershman, S.J. (2019). Adapting the flow of time with dopamine. Journal of Neurophysiology, 121, 1748-1760.
Cushman, F., & Gershman, S.J. (2019). Editor's introduction: computational approaches to social cognition. Topics in Cognitive Science, 11, 281-298. [Full issue].
Dorfman, H.M., Bhui, R., Hughes, B.L., & Gershman, S.J. (2019). Causal inference about good and bad outcomes. Psychological Science, 30, 516-525. [Supplementary materials] [code+data]
Kurdi, B., Gershman, S.J., & Banaji, M.R. (2019). Model-free and model-based learning processes in the updating of explicit and implicit evaluations. Proceedings of the National Academy of Sciences, 116, 6035-6044.
Gershman, S.J. (2019). How to never be wrong. Psychonomic Bulletin and Review, 26, 13-28.
Millner, A.J., den Ouden, H.E.M., Gershman, S.J., Glenn, C.R., Kearns, J., Bornstein, A.M., Marx, B.P., Keane, T.M., & Nock, M.K. (2019). Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive states. Journal of Abnormal Psychology, 128, 106-118.
Tiganj, Z., Gershman, S.J., Sederberg, P.B., & Howard, M.W. (2019). Estimating scale-invariant future in continuous time. Neural Computation, 31, 681-709.
Patzelt, E.H., Kool, W., Millner, A.J., & Gershman, S.J. (2019). The transdiagnostic structure of mental effort avoidance. Scientific Reports, 9, 1689.
Patzelt, E., Kool, W., Millner, A.J., & Gershman, S.J. (2019). Incentives boost model-based control across a range of severity on several psychiatric constructs. Biological Psychiatry, 85, 425-433.
Schulz, E. & Gershman, S.J. (2019). The algorithmic architecture of exploration in the human brain. Current Opinion in Neurobiology, 55, 7-14.
Wu, C.M., Schulz, E., & Gershman, S.J. (2019). Generalization as diffusion: human function learning on graphs. Proceedings of the 41st Annual Conference of the Cognitive Science Society.
Liu, S., Cushman, F.A., Gershman, S.J., Kool, W., & Spelke, E.S. (2019). Hard choices: Children’s understanding of the cost of action selection. Proceedings of the 41st Annual Conference of the Cognitive Science Society.
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S.J., & Doshi-Velez, F. (2019). Human evaluation of models built for interpretability. 7th AAAI Conference on Human Computation and Crowdsourcing.
2018
Lau, T., Pouncy, H.T., Gershman, S.J., & Cikara, M. (2018). Discovering social groups via latent structure learning. Journal of Experimental Psychology: General, 147, 1881-1891.
Gardner, M.P.H., Schoenbaum, G., & Gershman, S.J. (2018). Rethinking dopamine as generalized prediction error. Proceedings of the Royal Society B, 285, 20181645.
Bhui, R., & Gershman, S.J. (2018). Decision by sampling implements efficient coding of psychoeconomic functions. Psychological Review, 125, 985-1001.
Patzelt, E., Hartley, C.A., & Gershman, S.J. (2018). Computational phenotyping: using models to understand personality, development, and mental illness. Personality Neuroscience, 1, e18.
Gershman, S.J., & Tzovaras, B.G. (2018). Dopaminergic genes are associated with both directed and random exploration. Neuropsychologia, 120, 97-104.
Petter, E.A., Gershman, S.J., & Meck, W.H. (2018). Integrating models of interval timing and reinforcement learning. Trends in Cognitive Sciences, 22, 911-922.
Lage, I., Ross, A.S., Kim, B., Gershman, S.J., & Doshi-Velez, F. (2018). Human-in-the-loop interpretability prior. Advances in Neural Information Processing Systems 32.
Kool, W., Gershman, S.J., & Cushman, F.A. (2018). Planning complexity registers as a cost in metacontrol. Journal of Cognitive Neuroscience, 30, 1391-1404.
Millner, A.J., Gershman, S.J., Nock, M.K., & Ouden, H.D. (2018). Pavlovian control of escape and avoidance. Journal of Cognitive Neuroscience, 30, 1379-1390.
Gershman, S.J. (2018). The successor representation: its computational logic and neural substrates. Journal of Neuroscience, 38, 7193-7200.
Tomov, M.S., Dorfman, H.M., & Gershman, S.J. (2018). Neural computations underlying causal structure learning. Journal of Neuroscience, 38, 7143-7157.
Dasgupta, I., Schulz, E., Goodman, N.D., & Gershman, S.J. (2018). Remembrance of inferences past: amortization in human hypothesis generation. Cognition, 178, 67-81.
Babayan, B.M., Uchida, N., & Gershman, S.J. (2018). Belief state representation in the dopamine system. Nature Communications, 9, 1891.
Starkweather, C.K., Gershman, S.J., & Uchida, N. (2018). The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty. Neuron, 98, 616-629.
Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S.J., Kanwisher, N., Botvinick, M., & Fedorenko, E. (2018). Toward a universal decoder of linguistic meaning from brain activation. Nature Communications, 9, 963.
Blanchard, T.C., & Gershman, S.J. (2018). Pure correlates of exploration and exploitation in the human brain. Cognitive, Affective, & Behavioral Neuroscience, 18, 117-126.
Gershman, S.J. (2018). Deconstructing the human algorithms for exploration. Cognition, 173, 34-42.
Kool, W., & Cushman, F.A., & Gershman, S.J. (2018). Competition and cooperation between multiple reinforcement learning systems. In R.W. Morris & A. Bornstein (Eds.) Goal-Directed Decision Making: Computations and Neural Circuits. Elsevier.
Dasgupta, I., Smith, K.A., Schulz, E., Tenenbaum, J.B., & Gershman, S.J. (2018). Learning to act by integrating mental simulations and physical experiments. Proceedings of the 40th Annual Conference of the Cognitive Science Society.
Dasgupta, I., Guo, D., Stuhlmuller, A., Gershman, S.J., & Goodman, N.D. (2018). Evaluating compositionality in sentence embeddings. Proceedings of the 40th Annual Conference of the Cognitive Science Society.
Baumann, C., Singmann, H., Gershman, S.J., & von Helversen, B. (2018). Explaining human decision making in optimal stopping tasks. Proceedings of the 40th Annual Conference of the Cognitive Science Society.
Hunter, L.E. & Gershman, S.J. (2018). Reference-dependent preferences arise from structure learning. bioRxiv 252692.
2017
Lake, B.M., Ullman, T.D., Tenenbaum, J.B., & Gershman, S.J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.
Gershman, S.J. (2017). Dopamine, inference, and uncertainty. Neural Computation, 29, 3311-3326. [erratum]
Schulz, E., Tenenbaum, J.B., Duvenaud, D., Speekenbrink, M., & Gershman, S.J. (2017). Compositional inductive biases in function learning. Cognitive Psychology, 99, 44-79.
Gershman, S.J., Zhou, J., & Kommers, C. (2017). Imaginative reinforcement learning: computational principles and neural mechanisms. Journal of Cognitive Neuroscience, 29, 2103-2113.
Stachenfeld, K.L., Botvinick, M.M., & Gershman, S.J. (2017). The hippocampus as a predictive map. Nature Neuroscience, 20, 1643-1653. [supplement] [DeepMind Blog post]
Kool, W., Gershman, S.J., & Cushman, F.A. (2017). Cost-benefit arbitration between multiple reinforcement learning systems. Psychological Science, 28, 1321-1333. [supplement]
Saeedi, A., Kulkarni, T.D., Mansinghka, V.K., & Gershman, S.J. (2017). Variational particle approximations. Journal of Machine Learning Research, 18, 1-29. [github code]
Russek, E., Momennejad, I., Botvinick, M.M., Gershman, S.J., & Daw, N.D. (2017). Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLOS Computational Biology, 13, e1005768.
Momennejad, I., Russek, E., Cheong, J.H., Botvinick, M.M., Daw, N.D., & Gershman, S.J. (2017). The successor representation in human reinforcement learning. Nature Human Behaviour, 1, 680-692.
Linderman, S.W., & Gershman, S.J. (2017). Using computational theory to constrain statistical models of neural data. Current Opinion in Neurobiology, 46, 14-24.
Gershman, S.J. (2017). Predicting the past, remembering the future. Current Opinion in Behavioral Sciences, 17, 7-13.
Dasgupta, I., Schulz, E., & Gershman, S.J. (2017). Where do hypotheses come from? Cognitive Psychology, 96, 1-25.
Dasgupta, I., Schulz, E., Goodman, N.D., & Gershman, S.J. (2017). Amortized hypothesis generation. Proceedings of the 39th Annual Conference of the Cognitive Science Society.
Starkweather, C.K., Babayan, B.M., Uchida, N., & Gershman, S.J. (2017). Dopamine reward prediction errors reflect hidden state inference across time. Nature Neuroscience, 20, 581-589.
Gershman, S.J., Monfils, M.-H., Norman, K.A., & Niv, Y. (2017). The computational nature of memory modification. eLife, 6, e23763.
Gershman, S.J., Pouncy, H.T., & Gweon, H. (2017). Learning the structure of social influence. Cognitive Science, 41, 545-575.
Gershman, S.J. (2017). Context-dependent learning and causal structure. Psychonomic Bulletin & Review, 24, 557-565.
Gershman, S.J. (2017). Reinforcement learning and causal models. In M. Waldmann, Ed, Oxford Handbook of Causal Reasoning. Oxford University Press.
Gershman, S.J. & Beck, J.M. (2017). Complex probabilistic inference: from cognition to neural computation. In A. Moustafa (Ed.) Computational Models of Brain and Behavior. Wiley-Blackwell.
Gershman, S.J., Malmaud, J., & Tenenbaum, J.B. (2017). Structured representations of utility in combinatorial domains. Decision, 4, 67-86.
Thaker, P., Tenenbaum, J.B., & Gershman, S.J. (2017). Online learning of symbolic concepts. Journal of Mathematical Psychology, 77, 10-20.
Gershman, S.J. & Daw, N.D. (2017). Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology, 68, 101-128.
Tsividis, P.A., Pouncy, T., Xu, J.L., Tenenbaum, J.B., & Gershman, S.J. (2017). Human learning in Atari. AAAI Spring Symposium on Science of Intelligence: Computational Principles of Natural and Artificial Intelligence.
Gershman, S.J. (2017). On the blessing of abstraction. The Quarterly Journal of Experimental Psychology, 70, 361-365.
2016
Cikara, M., & Gershman, S.J. (2016). Medial prefrontal cortex updates its status. Neuron, 92, 937-939. [Preview of Kumaran et al. (2016)]
Gershman, S.J., Gerstenberg, T., Baker, C.L., & Cushman, F.A. (2016). Plans, habits, and theory of mind. PLOS One, 11, e0162246.
Kool, W., Cushman, F.A., & Gershman, S.J. (2016). When does model-based control pay off? PLOS Computational Biology, 12, e1005090. [Supplemental Information] [Code and data]
Gershman, S.J., Tenenbaum, J.B., & Jäkel, F.J. (2016). Discovering hierarchical motion structure. Vision Research, 126, 232-241. [code] [demos]
Pereira, F., Gershman, S.J., Ritter, S., & Botvinick, M.M. (2016). A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data. Cognitive Neuropsychology, 33, 175-190.
Schulz, E., Tenenbaum, J.B., Duvenaud, D., Speekenbrink, M., & Gershman, S.J. (2016). Probing the compositionality of intuitive functions. Advances in Neural Information Processing Systems, 29.
Ullman, T.D., Siegel, M., Tenenbaum, J.B., & Gershman, S.J. (2016). Coalescing the vapors of human experience into a viable and meaningful comprehension. Proceedings of the 38th Annual Conference of the Cognitive Science Society.
Batmanghelich, K., Saeedi, A., Narasimhan, K., & Gershman, S.J. (2016). Nonparametric spherical topic modeling with word embeddings. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.
Tervo, D.G.R., Tenenbaum, J.B., & Gershman, S.J. (2016). Toward the neural implementation of structure learning. Current Opinion in Neurobiology, 37, 99-105.
Gershman, S.J. (2016). Empirical priors for reinforcement learning models. Journal of Mathematical Psychology, 71, 1-6.
2015
Gershman, S.J. (2015). A unifying probabilistic view of associative learning. PLOS Computational Biology, 11, e1004567.
Gershman, S.J., Norman, K.A., & Niv, Y. (2015). Discovering latent causes in reinforcement learning. Current Opinion in Behavioral Sciences, 5, 43-50.
Gershman, S.J., Horvitz, E.J., & Tenenbaum, J.B. (2015). Computational rationality: A converging paradigm for intelligence in brains, minds and machines. Science, 349, 273-278.
Gershman, S.J. & Hartley, C.A. (2015). Individual differences in learning predict the return of fear. Learning & Behavior, 43, 243-250. [Supplementary Materials]
Gershman, S.J. (2015). Do learning rates adapt to the distribution of rewards? Psychonomic Bulletin & Review, 22, 1320-1327. [data]
Gershman, S.J. & Tenenbaum, J.B. (2015). Phrase similarity in humans and machines. Proceedings of the 37th Annual Conference of the Cognitive Science Society.
Schulz, E., Tenenbaum, J.B., Reshef, D.N., Speekenbrink, M., & Gershman, S.J. (2015). Assessing the perceived predictability of functions. Proceedings of the 37th Annual Conference of the Cognitive Science Society.
Niv, Y., Daniel, R., Geana, A., Gershman, S.J., Leong, Y.C., Radulescu, A., & Wilson, R.C. (2015). Reinforcement learning in multidimensional environments relies on attention mechanisms. Journal of Neuroscience, 35, 8145-8157.
Gershman, S.J. & Niv, Y. (2015). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science, 1-25.
Huys, Q.J.M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S.J., Dayan, P., & Roiser, J.P. (2015). The interplay of approximate planning strategies. Proceedings of the National Academy of Sciences, 112, 3098-3103. [commentary by Daniel, Schuck & Niv]
Gershman, S.J., Frazier, P.I., & Blei, D.M. (2015). Distance dependent infinite latent feature models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 334-345. [Supplementary Materials] [code]
Austerweil, J.L., Gershman, S.J., Tenenbaum, J.B., & Griffiths, T.L. (2015). Structure and flexibility in Bayesian models of cognition. In J.R. Busemeyer, J.T. Townsend, Z. Wang, & A. Eidels, Eds, Oxford Handbook of Computational and Mathematical Psychology. Oxford University Press.
2014
Stachenfeld, K.L., Botvinick, M.M., & Gershman, S.J. (2014). Design principles of the hippocampal cognitive map. Advances in Neural Information Processing Systems 27. [Supplementary Materials]
Gershman, S.J., Radulescu, A., Norman, K.A., & Niv, Y. (2014). Statistical computations underlying the dynamics of memory updating. PLoS Computational Biology, 10, e1003939. [code]
Gershman, S.J. (2014). The penumbra of learning: A statistical theory of synaptic tagging and capture. Network: Computation in Neural Systems, 25, 97-115.
Soto, F.A., Gershman, S.J., & Niv, Y. (2014). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychological Review, 121, 526-558.
Gershman, S.J., Blei, D.M., Norman, K.A., & Sederberg, P.B. (2014). Decomposing spatiotemporal brain patterns into topographic latent sources. NeuroImage, 98, 91-102. [code]
Gershman, S.J. & Goodman, N.D. (2014). Amortized inference in probabilistic reasoning. Proceedings of the 36th Annual Conference of the Cognitive Science Society.
Tsividis, P., Gershman, S.J., Tenenbaum, J.B., & Schulz, L. (2014). Information selection in noisy environments with large action spaces. Proceedings of the 36th Annual Conference of the Cognitive Science Society.
Feng, S.F., Schwemmer, M., Gershman, S.J., & Cohen, J.D. (2014). Multitasking vs. multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cognitive, Affective, and Behavioral Neuroscience, 14, 129-146.
Gershman, S.J. (2014). Dopamine ramps are a consequence of reward prediction errors. Neural Computation, 26, 467-471.
Gershman, S.J., Markman, A.B., & Otto, A.R. (2014). Retrospective revaluation in sequential decision making: a tale of two systems. Journal of Experimental Psychology: General, 143, 182-194.
Gershman, S.J., Moustafa, A.A., & Ludvig, E.A. (2014). Time representation in reinforcement learning models of the basal ganglia. Frontiers in Computational Neuroscience. doi: 10.3389/fncom.2013.00194
2013
Gershman, S.J. (2013). Computation with dopaminergic modulation. In Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer.
Gershman, S.J. (2013). Bayesian behavioral data analysis. In Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer.
Gershman, S.J., Jones, C.E., Norman, K.A., Monfils, M.-H., & Niv, Y. (2013). Gradual extinction prevents the return of fear: Implications for the discovery of state. Frontiers in Behavioral Neuroscience. doi: 10.3389/fnbeh.2013.00164. [article in Footnote magazine]
Detre, G.J., Natarajan, A., Gershman, S.J., & Norman, K.A. (2013). Moderate levels of activation lead to forgetting in the think/no-think paradigm. Neuropsychologia, 51 2371-2388. [Supplementary Materials] [code]
Christakou, A., Gershman, S.J., Niv, Y., Simmons, A., Brammer, M., & Rubia, K. (2013). Neural and psychological maturation of decision-making in adolescence and young adulthood. Journal of Cognitive Neuroscience, 25, 1807-1823.
Gershman, S.J. & Niv, Y. (2013). Perceptual estimation obeys Occam's razor. Frontiers in Psychology, 23, doi: 10.3389/fpsyg.2013.00623.
Gershman, S.J., Schapiro, A.C., Hupbach, A., & Norman, K.A. (2013). Neural context reinstatement predicts memory misattribution. Journal of Neuroscience, 33, 8590-8595.
Otto, A.R., Gershman, S.J., Markman, A.B., & Daw, N.D. (2013). The curse of planning: Dissecting multiple reinforcement learning systems by taxing the central executive. Psychological Science, 24, 751-761. [Supplementary Materials]
Gershman, S.J., Jäkel, F.J., & Tenenbaum, J.B. (2013). Bayesian vector analysis and the perception of hierarchical motion. Proceedings of the 35th Annual Conference of the Cognitive Science Society.
Wingate, D., Diuk, C., O'Donnell, T., Tenenbaum, J.B., & Gershman, S.J. (2013). Compositional policy priors. MIT CSAIL Technical Report 2013-007.
Gershman, S.J. (2013). Memory modification in the brain: computational and experimental investigations. Ph.D Thesis, Princeton University, Department of Psychology.
2012
Gershman, S.J. & Niv, Y (2012). Exploring a latent cause model of classical conditioning. Learning & Behavior, 40, 255-268. [Supplementary Materials] [Erratum]
[code]
Gershman, S.J., Hoffman, M.D., & Blei, D.M. (2012). Nonparametric variational inference. Proceedings of the 29th International Conference on Machine Learning. [code]
Gershman, S.J., Moore, C.D., Todd, M.T., Norman, K.A., & Sederberg, P.B. (2012). The successor representation and temporal context. Neural Computation, 24, 1553-1568.
Gershman, S.J. & Blei, D.M. (2012). A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology, 56, 1-12. [correction]
Gershman, S.J. & Daw, N.D. (2012). Perception, action and utility: the tangled skein. In M. Rabinovich, K. Friston, P. Varona (Ed.), Principles of Brain Dynamics: Global State Interactions. MIT Press.
Gershman, S.J., Vul, E., & Tenenbaum, J.B. (2012). Multistability and perceptual inference. Neural Computation, 24, 1-24.
2011
Gershman, S.J., Blei, D.M., Pereira, F., & Norman, K.A. (2011). A topographic latent source model for fMRI data. NeuroImage, 57, 89-100.
Sederberg, P.B., Gershman, S.J., Polyn, S.M., & Norman, K.A. (2011). Human memory reconsolidation can be explained using the Temporal Context Model. Psychonomic Bulletin and Review, 18, 455-468.
Daw, N.D., Gershman, S.J., Seymour, B., Dayan, P., & Dolan, R.J. (2011). Model-based influences on humans' choices and striatal prediction errors. Neuron, 69, 1204-1215. [Supplementary Materials]
2010
Gershman, S.J. & Wilson, R.C. (2010). The neural costs of optimal control. Advances in Neural Information Processing Systems 23.
Gershman, S.J, Cohen, J.D., & Niv, Y. (2010). Learning to selectively attend. Proceedings of the 32nd Annual Conference of the Cognitive Science Society.
Gershman, S.J & Niv, Y. (2010). Learning latent structure: Carving nature at its joints. Current Opinion in Neurobiology, 20, 1-6.
Gershman, S.J., Blei, D.M., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117, 197-209.
2009
Gershman, S.J., Vul, E., & Tenenbaum, J.B. (2009). Perceptual multistability as Markov chain Monte Carlo inference. Advances in Neural Information Processing Systems 22.
Socher, R., Gershman, S.J., Perotte, A., Sederberg, P.B., Blei, D.M., & Norman, K.A. (2009). A Bayesian analysis of dynamics in free recall. Advances in Neural Information Processing Systems 22. [code+data]
Gershman, S.J., Pesaran, B., & Daw, N.D. (2009). Human reinforcement learning subdivides structured action spaces by learning effector-specific values. Journal of Neuroscience, 29, 13524-13531. [Supplementary Materials]