Samuel Gershman

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Coauteurs

Joshua B. TenenbaumMITAdresse e-mail validée de mit.edu
Yael NivProfessor of Psychology and Neuroscience, Princeton UniversityAdresse e-mail validée de princeton.edu
Eric SchulzHelmholtz MunichAdresse e-mail validée de helmholtz-munich.de
Kenneth A. NormanProfessor of Psychology and Neuroscience, Princeton UniversityAdresse e-mail validée de princeton.edu
Matthew BotvinickGoogle DeepMind, Yale Law School, University College LondonAdresse e-mail validée de google.com
Tomer UllmanAssistant Professor, HarvardAdresse e-mail validée de fas.harvard.edu
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityAdresse e-mail validée de princeton.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityAdresse e-mail validée de columbia.edu
Fiery CushmanPsychology, Harvard UniversityAdresse e-mail validée de fas.harvard.edu
Naoshige UchidaProfessor of Molecular and Cellular Biology, Harvard UniversityAdresse e-mail validée de mcb.harvard.edu
Kimberly Lauren StachenfeldResearch Scientist, Google DeepMindAdresse e-mail validée de google.com
Rahul BhuiMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Wouter KoolAssistant Professor at Washington University in St. LouisAdresse e-mail validée de wustl.edu
Ishita DasguptaResearch Scientist, DeepMindAdresse e-mail validée de google.com
Momchil S. TomovHarvard UniversityAdresse e-mail validée de g.harvard.edu
John G. MikhaelHarvardAdresse e-mail validée de hms.harvard.edu
Noah D. GoodmanStanford UniversityAdresse e-mail validée de stanford.edu
Lucy LaiHarvard University, Okinawa Institute of Science and TechnologyAdresse e-mail validée de g.harvard.edu
Brenden M. LakeAssociate Professor, New York UniversityAdresse e-mail validée de nyu.edu
Per B. SederbergProfessor of Psychology, University of VirginiaAdresse e-mail validée de virginia.edu

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Samuel Gershman

Professor, Harvard University

Adresse e-mail validée de fas.harvard.edu - Page d'accueil

Computational neuroscience cognitive science machine learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40, e253, 2017	3418	2017
Model-based influences on humans' choices and striatal prediction errors ND Daw, SJ Gershman, B Seymour, P Dayan, RJ Dolan Neuron 69 (6), 1204-1215, 2011	2061	2011
The hippocampus as a predictive map KL Stachenfeld, MM Botvinick, SJ Gershman Nature Neuroscience 20, 1643-1653, 2017	997	2017
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines SJ Gershman, EJ Horvitz, JB Tenenbaum Science 349 (6245), 273-278, 2015	869	2015
A tutorial on Bayesian nonparametric models SJ Gershman, DM Blei Journal of Mathematical Psychology 56, 1-12, 2012	749	2012
Reinforcement learning and episodic memory in humans and animals: an integrative framework SJ Gershman, ND Daw Annual review of psychology 68 (1), 101-128, 2017	560	2017
The successor representation in human reinforcement learning I Momennejad, EM Russek, JH Cheong, MM Botvinick, ND Daw, ... Nature human behaviour 1 (9), 680-692, 2017	472	2017
Context, learning, and extinction SJ Gershman, DM Blei, Y Niv Psychological Review 117 (1), 197-209, 2010	468	2010
Reinforcement learning in multidimensional environments relies on attention mechanisms Y Niv, R Daniel, A Geana, SJ Gershman, YC Leong, A Radulescu, ... Journal of Neuroscience 35 (21), 8145-8157, 2015	448	2015
Amortized Inference in Probabilistic Reasoning SJ Gershman, ND Goodman Proceedings of the 36th Annual Cognitive Science Society, 2013	427	2013
The curse of planning: Dissecting multiple reinforcement learning systems by taxing the central executive AR Otto, SJ Gershman, AB Markman, ND Daw Psychological Science 24 (5), 751-761, 2013	414	2013
Toward a universal decoder of linguistic meaning from brain activation F Pereira, B Lou, B Pritchett, S Ritter, SJ Gershman, N Kanwisher, ... Nature communications 9 (1), 963, 2018	386	2018
Predictive representations can link model-based reinforcement learning to model-free mechanisms EM Russek, I Momennejad, MM Botvinick, SJ Gershman, ND Daw PLoS computational biology 13 (9), e1005768, 2017	360	2017
Learning latent structure: carving nature at its joints SJ Gershman, Y Niv Current Opinion in Neurobiology 20 (2), 251-256, 2010	359	2010
Cost-benefit arbitration between multiple reinforcement-learning systems W Kool, SJ Gershman, FA Cushman Psychological science 28 (9), 1321-1333, 2017	313	2017
Retrospective revaluation in sequential decision making: A tale of two systems SJ Gershman, AB Markman, AR Otto Journal of Experimental Psychology: General 143, 182-194, 2014	306	2014
Interplay of approximate planning strategies QJM Huys, N Lally, P Faulkner, N Eshel, E Seifritz, SJ Gershman, ... Proceedings of the National Academy of Sciences 112 (10), 3098-3103, 2015	305	2015
Deconstructing the human algorithms for exploration SJ Gershman Cognition 173, 34-42, 2018	276	2018
Deep successor reinforcement learning TD Kulkarni, A Saeedi, S Gautam, SJ Gershman arXiv preprint arXiv:1606.02396, 2016	274	2016
A unified framework for dopamine signals across timescales HGR Kim, AN Malik, JG Mikhael, P Bech, I Tsutsui-Kimura, F Sun, ... Cell 183 (6), 1600-1616. e25, 2020	245	2020

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