Working papers

  • Fan, H., Callaway, F., & Gershman, S.J. (submitted). Uncertainty-driven exploration during planning.
  • Liu, S., & Gershman, S.J. (submitted). Action subsampling supports policy compression in large action spaces.
  • Kumar, T., Bordelon, B., Pehlevan, C., Murthy, V.N., & Gershman, S.J. (submitted). Do mice grok? glimpses of hidden progress during overtraining in sensory cortex.
  • Xiang, Y., Gershman, S.J., & Gerstenberg, T. (submitted). A signaling theory of self-handicapping.
  • Xiang, Y., Dorst, K., & Gershman, S.J. (submitted). On the robustness and provenance of the gambler's fallacy.
  • 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). Bayesian estimation yields anti-Weber variability.
  • 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.
  • 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.
  • 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.
  • 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

  • Binz et al. (in press). How should the advent of large language models affect the practice of science? Proceedings of the National Academy of Science.
  • Qian, L., Burrell, M., Hennig, J.A., Matias, S., Murthy, V.N., Gershman, S.J., Uchida, N. (in press). The role of prospective contingency in the control of behavior and dopamine signals during associative learning. Nature Neuroscience.
  • 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.

    2025

  • Xiang, Y., Landy, J., Cushman, F.A., Velez, N., & Gershman, S.J. (2025). People reward others based on their willingness to exert effort. Journal of Experimental Social Psychology, 116, 104699. [code+data]
  • Sosa, F., Gershman, S.J., & Ullman, T. (2025). Blending simulation and abstraction for physical reasoning. Cognition, 254, 105995.

    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.
  • Orchinik, R., Dubey, R., Gershman, S.J., Powell, D., Bhui, R. (2024). Learning from and about scientists: Consensus messaging shapes perceptions of climate change and climate scientists. PNAS Nexus, 3, 485.
  • Allen, K., Brändle, F., ... Schulz, E. (2024) Using games to understand the mind. Nature Human Behaviour, 8, 1035-1043.
  • 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.
  • Nguyen, T., Bezdek, M., Gershman, S.J., Bobick, A.F., Braver, T., & Zacks, J.M. (2024). Modeling human activity comprehension at human scale: Prediction, segmentation, and categorization. PNAS Nexus, 3, 459.
  • 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]