Seuraa
Samuel Gershman
Samuel Gershman
Vahvistettu sähköpostiosoite verkkotunnuksessa fas.harvard.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Building machines that learn and think like people
BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman
Behavioral and brain sciences 40, e253, 2017
30182017
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
18952011
The hippocampus as a predictive map
KL Stachenfeld, MM Botvinick, SJ Gershman
Nature Neuroscience 20, 1643-1653, 2017
8522017
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
SJ Gershman, EJ Horvitz, JB Tenenbaum
Science 349 (6245), 273-278, 2015
7702015
A tutorial on Bayesian nonparametric models
SJ Gershman, DM Blei
Journal of Mathematical Psychology 56, 1-12, 2012
7212012
Reinforcement learning and episodic memory in humans and animals: an integrative framework
SJ Gershman, ND Daw
Annual review of psychology 68, 101-128, 2017
5032017
Context, learning, and extinction
SJ Gershman, DM Blei, Y Niv
Psychological Review 117 (1), 197-209, 2010
4232010
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
4172017
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
3952015
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
3782013
Amortized Inference in Probabilistic Reasoning
SJ Gershman, ND Goodman
Proceedings of the 36th Annual Cognitive Science Society, 2013
3742013
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
3402018
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
3292017
Learning latent structure: carving nature at its joints
SJ Gershman, Y Niv
Current Opinion in Neurobiology 20 (2), 251-256, 2010
3232010
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
2912014
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
2822015
Cost-benefit arbitration between multiple reinforcement-learning systems
W Kool, SJ Gershman, FA Cushman
Psychological science 28 (9), 1321-1333, 2017
2642017
Deep successor reinforcement learning
TD Kulkarni, A Saeedi, S Gautam, SJ Gershman
arXiv preprint arXiv:1606.02396, 2016
2452016
Deconstructing the human algorithms for exploration
SJ Gershman
Cognition 173, 34-42, 2018
2402018
The successor representation: its computational logic and neural substrates
SJ Gershman
Journal of Neuroscience 38 (33), 7193-7200, 2018
2082018
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Artikkelit 1–20