Publication

Found 195 results
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2019
Gershman, S. J. The Generative Adversarial Brain. Frontiers in Artificial Intelligence 2, (2019).
Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260PDF icon CBMM-Memo-101.pdf (12.78 MB)
Leonard, J. A., Garcia, A. & Schulz, L. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305
Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs. Biological Psychiatry 85, 425 - 433 (2019).
Feather, J., Durango, A., Gonzalez, R. & McDermott, J. H. Metamers of neural networks reveal divergence from human perceptual systems. NIPS 2019 (2019). at <https://papers.nips.cc/paper/9198-metamers-of-neural-networks-reveal-divergence-from-human-perceptual-systems>PDF icon Feather_etal_2019_NeurIPS_metamers.pdf (4.7 MB)
Barbu, A. et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks. Vision Science Society (2019).
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human oneshot learning suggests a hierarchical architecture different from deep convolutional neural networks . Vision Science Society (2019). doi:10.1167/19.10.28d
Chu, J., Gauthier, J., Levy, R., Tenenbaum, J. B. & Schulz, L. Query-guided visual search . 41st Annual conference of the Cognitive Science Society (2019).
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. The transdiagnostic structure of mental effort avoidance. Scientific Reports 9, (2019).
Han, C., Mao, J., Gan, C., Tenenbaum, J. B. & Wu, J. Visual Concept-Metaconcept Learning. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 8745-visual-concept-metaconcept-learning.pdf (1.92 MB)
2018
Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).PDF icon CBMM-Memo-085.pdf (2.2 MB)
Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).
Madhavan, R. et al. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
Kool, W., Gershman, S. J. & Cushman, F. A. Planning Complexity Registers as a Cost in Metacontrol. Journal of Cognitive Neuroscience 30, 1391 - 1404 (2018).
Hu, S. et al. Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes. Cell Reports 25, 2635 - 2642.e5 (2018).
Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)
2017
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and Brain Sciences 40, e253 (2017).
Bramley, N., Mayrhofer, R., Gerstenberg, T. & Lagnado, D. A. Causal learning from interventions and dynamics in continuous time. Cognitive Science Conference (2017).PDF icon Bramley et al. - 2017 - Causal learning from interventions and dynamics in.pdf (1.78 MB)
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).

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