Sam Gershman
"There is something inside of me. What is it?"
- Vincent van Gogh
CV
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. Currently, I'm a graduate student at Princeton University, pursuing a PhD in psychology and neuroscience.
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'm currently involved in performing experiments with humans and animals to test the predictions of these models.
Publications
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- Gershman, S.J. & Niv, Y. (in preparation). Categorical perception obeys Occam's razor.
- Gershman, S.J. & Niv, Y. (in preparation). Novelty and inductive generalization in human reinforcement learning.
- Gershman, S.J. & Niv, Y (submitted). Exploring a latent cause model of classical conditioning.
- Otto, A.R., Gershman, S.J., Daw, N.D., & Markman, A.B. (submitted). Dissecting multiple reinforcement learning systems by taxing the central executive.
- Gershman, S.J., Frazier, P.I., & Blei, D.M. (submitted). Distance dependent infinite latent feature models.
- Gershman, S.J., Moore, C.D., Todd, M.T., Norman, K.A., & Sederberg, P.B. (in press). The sucessor representation and temporal context. Neural Computation.
- Gershman, S.J. & Blei, D.M. (in press). A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology.
- Gershman, S.J. & Daw, N.D. (2012). Perception, action and utility: the tangled skein. In M. Rabinovich, K. Friston & P. Varona, Eds, 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]
Collaborators
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