Sam Gershman

"There is something inside of me. What is it?"
- Vincent van Gogh

In July 2015 I will be starting as an assistant professor in the Department of Psychology and Center for Brain Science at Harvard.
Please contact me if you are interested in doing graduate or postdoctoral work in my lab.

About me

I grew up in Chicago, and did my undergraduate studies at Columbia University in New York, where I majored in neuroscience and behavior. After graduating in 2007, I spent a year at the Center for Neural Science at New York University, where I studied reinforcement learning in humans and monkeys. I received my Ph.D in psychology and neuroscience from Princeton University in 2013. I'm currently a postdoctoral fellow at MIT in Josh Tenenbaum's Computational Cognitive Science Group.

Research

The focus of my work is on computational models of learning and memory. In particular, I'm interested in to what extent we can understand the brain as performing statistical inference. I test the predictions of these models in humans and animals using behavioral and brain imaging methods. You can read my CV here.

Popular press

  • How can we unlearn fear? Gradually, it turns out. Footnote (December 30, 2013).
  • The exploitative economics of academic publishing. Footnote (March 18, 2014).

    Papers

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    Working papers

  • Gershman, S.J., Malmaud, J., & Tenenbaum, J.B. (in preparation). Structured representations of utility in combinatorial domains.
  • Gershman, S.J., Monfils, M.-H., Norman, K.A., & Niv, Y. (in preparation). The computational nature of memory reconsolidation.
  • Huys, Q.J.M., Lally, N., Faulkner, P., Seifritz, E., Gershman, S.J., Dayan, P., & Roiser, J.P. (submitted). The interplay of approximate planning strategies.
  • Niv, Y., Daniel, R., Geana, A., Gershman, S.J., Leong, Y.C., Radulescu, A., & Wilson, R.C. (submitted). Reinforcement learning in multidimensional environments relies on attention mechanisms.
  • Gershman, S.J. & Hartley, C.A. (submitted). Individual differences in learning predict the return of fear.
  • Gershman, S.J. (submitted). Do learning rates adapt to the distribution of rewards?
  • Kulkarni, T.D., Saeedi, A., & Gershman, S.J. (submitted). Variational particle approximations. [Ising model code] [github code]
  • Gershman, S.J., Radulescu, A., Norman, K.A., & Niv, Y. (submitted). Statistical computations underlying the dynamics of memory updating.

    Publications

  • Gershman, S.J. & Niv, Y. (in press). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science.
  • Gershman, S.J., Frazier, P.I., & Blei, D.M. (in press). Distance dependent infinite latent feature models. IEEE Transactions on Pattern Analysis and Machine Intelligence. [Supplementary Materials] [code]
  • Soto, F.A., Gershman, S.J., & Niv, Y. (in press). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychological Review.
  • Gershman, S.J. (in press). The penumbra of learning: A statistical theory of synaptic tagging and capture. Network: Computation in Neural Systems.
  • Austerweil, J.L., Gershman, S.J., Tenenbaum, J.B., & Griffiths, T.L. (in press). 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.
  • 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]

    Software

    Disclaimer: Unless otherwise noted, the software provided below is for academic research purposes only. I provide no guarantees whatsoever. All software is written in Matlab, unless otherwise specified.

    Collaborators

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