Sam J. Gershman

Affiliations: 
Princeton University, Princeton, NJ 
 Psychology Harvard University, Cambridge, MA, United States 
Area:
Cognitive & computational neuroscience
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"Sam Gershman"
Mean distance: 13.03 (cluster 23)
 
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Parents

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Ken A. Paller research assistant 2004-2005 Northwestern
Hedy Kober research assistant 2005-2007 Columbia
Kevin Nicholas Ochsner research assistant 2005-2007 Columbia
Tor D. Wager research assistant 2005-2007 Columbia
Kenneth A. Norman grad student 2013 Princeton
 (Memory modification in the brain: Computational and experimental investigations.)
Yael Niv grad student 2009-2013 Harvard
 (Memory modification in the brain: Computational and experimental investigations.)
Joshua Tenenbaum post-doc MIT
Nathaniel D. Daw research scientist 2007-2008 NYU
Bijan Pesaran research scientist 2007-2008 NYU

Children

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Zhenglong Zhou research assistant 2018-2019
Prashant C. Raju research assistant 2020-2021 Harvard
Hayley M. Dorfman grad student
Edward H. Patzelt grad student
Lucy Lai grad student 2018-
Rahul Bhui post-doc Harvard
Honi Sanders post-doc 2016- Harvard
Wouter Kool post-doc 2015-2019 Harvard
BETA: Related publications

Publications

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Yang S, Bill J, Drugowitsch J, et al. (2021) Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports. 11: 3714
Gershman SJ, Guitart-Masip M, Cavanagh JF. (2021) Neural signatures of arbitration between Pavlovian and instrumental action selection. Plos Computational Biology. 17: e1008553
Tomov MS, Schulz E, Gershman SJ. (2021) Multi-task reinforcement learning in humans. Nature Human Behaviour
Dasgupta I, Gershman SJ. (2021) Memory as a Computational Resource. Trends in Cognitive Sciences
Pouncy T, Tsividis P, Gershman SJ. (2021) What Is the Model in Model-Based Planning? Cognitive Science. 45: e12928
Gershman SJ, Balbi PE, Gallistel CR, et al. (2021) Reconsidering the evidence for learning in single cells. Elife. 10
Dasgupta I, Guo D, Gershman SJ, et al. (2020) Analyzing Machine-Learned Representations: A Natural Language Case Study. Cognitive Science. 44: e12925
Kim HR, Malik AN, Mikhael JG, et al. (2020) A Unified Framework for Dopamine Signals across Timescales. Cell
Cohen AO, Nussenbaum K, Dorfman HM, et al. (2020) The rational use of causal inference to guide reinforcement learning strengthens with age. Npj Science of Learning. 5: 16
Bill J, Pailian H, Gershman SJ, et al. (2020) Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences of the United States of America. 117: 24581-24589
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