Working Papers

  • Gershman, S.J. & Schoenbaum, G. (submitted). Rethinking dopamine prediction errors.
  • Dasgupta, I., Schulz, E., Goodman, N.D., & Gershman, S.J. (submitted). Remembrance of inferences past.
  • Gershman, S.J. (submitted). Similarity as inference.
  • Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S.J., O'Brien, D., Schieber, S., Waldo, J., Weinberger, D., & Wood, A. (submitted). Accountability of AI under the law: the role of explanation.
  • Kool, W., Gershman, S.J., & Cushman, F.A. (submitted). Planning complexity registers as a cost in metacontrol.
  • Gershman, S.J., Froehlich, F., Botvinick, M.M., & Daw, N.D. (submitted). A predictive map in the human hippocampus.
  • Patzelt, E., Kool, W., Millner, A.J., & Gershman, S.J. (submitted). Model-based control across the psychopathology spectrum: impaired, but responsive to incentives.
  • Patzelt, E., Hartley, C.A., & Gershman, S.J. (submitted). Computational phenotyping: using models to understand personality, development, and mental illness.
  • Bhui, R., & Gershman, S.J. (submitted). Decision by sampling implements efficient coding of psychoeconomic functions.
  • Bhui, R., & Gershman, S.J. (submitted). Paradoxical effects of persuasive messages.
  • Chang, L.W., Gershman, S.J., & Cikara, M. (submitted). Divisive normalization drives transitivity violations in social choice.
  • Tomov, M.S., Dorfman, H.M., & Gershman, S.J. (submitted). Neural computations underlying causal structure learning.
  • Babayan, B.M., Uchida, N., & Gershman, S.J. (submitted). Belief state representation in the dopamine system.
  • Starkweather, C.K., Gershman, S.J., & Uchida, N. (submitted). Medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty.
  • Leon-Villagra, P., Schulz, E., Speekenbrink, M., Gershman, S.J., & Lucas, C.G. (submitted). One-shot compositional function learning.
  • Christakou, A., Gershman, S.J., Simmons, A., Murphy, C., Giampetro, V., Brammer, M., & Rubia, K. (submitted). Atypical maturation of decision-making in adolescent males with ADHD.
  • Lau, T., Pouncy, H.T., Gershman, S.J., & Cikara, M. (submitted). Discovering social groups via latent structure learning.
  • Gershman, S.J. (submitted). How to never be wrong.

    In Press

  • Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S.J., Kanwisher, N., Botvinick, M., & Fedorenko, E. (in press). Toward a universal decoder of linguistic meaning from brain activation. Nature Communications.
  • Blanchard, T.C., & Gershman, S.J. (in press). Pure correlates of exploration and exploitation in the human brain. Cognitive, Affective, & Behavioral Neuroscience.
  • Millner, A.J., Gershman, S.J., Nock, M.K., & Ouden, H.D. (in press). Pavlovian control of escape and avoidance. Journal of Cognitive Neuroscience.
  • Lake, B.M., Ullman, T.D., Tenenbaum, J.B., & Gershman, S.J. (in press). Building machines that learn and think like people. Behavioral and Brain Sciences.


  • 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.


  • Gershman, S.J. (2017). Dopamine, inference, and uncertainty. Neural Computation, 29, 3311-3326.
  • 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.


  • 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.


  • 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.
  • 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.


  • 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


  • 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.


  • Gershman, S.J. & Niv, Y (2012). Exploring a latent cause model of classical conditioning. Learning & Behavior, 40, 255-268. [Supplementary Materials] [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.


  • 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]


  • 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.


  • 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]