| Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40, e253, 2017 | 4350 | 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 | 2260 | 2011 |
| The hippocampus as a predictive map KL Stachenfeld, MM Botvinick, SJ Gershman Nature Neuroscience 20, 1643-1653, 2017 | 1189 | 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 | 1006 | 2015 |
| A tutorial on Bayesian nonparametric models SJ Gershman, DM Blei Journal of Mathematical Psychology 56, 1-12, 2012 | 813 | 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 | 646 | 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 | 540 | 2017 |
| Context, learning, and extinction SJ Gershman, DM Blei, Y Niv Psychological Review 117 (1), 197-209, 2010 | 518 | 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 | 493 | 2015 |
| Amortized Inference in Probabilistic Reasoning SJ Gershman, ND Goodman Proceedings of the 36th Annual Cognitive Science Society, 2013 | 485 | 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 | 458 | 2018 |
| 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 | 442 | 2013 |
| Learning latent structure: carving nature at its joints SJ Gershman, Y Niv Current Opinion in Neurobiology 20 (2), 251-256, 2010 | 401 | 2010 |
| 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 | 393 | 2017 |
| Cost-benefit arbitration between multiple reinforcement-learning systems W Kool, SJ Gershman, FA Cushman Psychological science 28 (9), 1321-1333, 2017 | 356 | 2017 |
| 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 | 340 | 2015 |
| 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 | 323 | 2014 |
| Deconstructing the human algorithms for exploration SJ Gershman Cognition 173, 34-42, 2018 | 321 | 2018 |
| 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 | 302 | 2020 |
| The successor representation: its computational logic and neural substrates SJ Gershman Journal of Neuroscience 38 (33), 7193-7200, 2018 | 302 | 2018 |
Joshua B. TenenbaumMITVerified email at mit.edu
