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McNamee, D., Stachenfeld, K., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal-hippocampal system. Nature Neuroscience (2021). doi:10.1038/s41593-021-00831-7
Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <>PDF icon 1912.04783.pdf (6.69 MB)
Poggio, T. From Associative Memories to Powerful Machines. (2021).PDF icon v1.0 (1.01 MB)PDF icon v1.3Section added August 6 on self attention (3.9 MB)
Poggio, T. From Marr’s Vision to the Problem of Human Intelligence. (2021).PDF icon CBMM-Memo-118.pdf (362.19 KB)
Wang, B. & Ponce, C. R. A Geometric Analysis of Deep Generative Image Models and Its Applications. Proc. International Conference on Learning Representations, 2021 (2021).
Sani, I. et al. The human endogenous attentional control network includes a ventro-temporal cortical node. Nature Communications 12, (2021).
Yang, S., Bill, J., Drugowitsch, J. & Gershman, S. J. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports 11, (2021).
Zhang, M., Badkundri, R., Talbot, M. B., Zawar, R. & Kreiman, G. Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206PDF icon 2104.02206.pdf (2.25 MB)
Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).PDF icon CBMM-Memo-120.pdf (2.83 MB)
Yang, Z. & Freiwald, W. A. Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face areaSignificance. Proceedings of the National Academy of Sciences 118, (2021).
Conwell, C. et al. Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex. Cosyne (2021).
Zaslavsky, N., Maldonado, M. & Culbertson, J. Let's talk (efficiently) about us: Person systems achieve near-optimal compression. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).
Anzellottti, S., Houlihan, S. Dae, Liburd, Jr, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2021). doi:10.1037/emo0000685PDF icon Anzellotti 2021 Emotion.pdf (1.08 MB)
Weisholtz, D. S. et al. Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract. Social Cognitive and Affective Neuroscience (2021). doi:10.1093/scan/nsab134
Ross, C., Barbu, A. & Katz, B. Measuring Social Biases in Grounded Vision and Language Embeddings. (2021).PDF icon CBMM-Memo-126.pdf (1.32 MB)
Ross, C., Katz, B. & Barbu, A. Measuring Social Biases in Grounded Vision and Language Embeddings. NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics) (2021).
Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).
Wang, J., Tao, A., Anderson, W. S., Madsen, J. R. & Kreiman, G. Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Reports 36, 109585 (2021).
Allen, K. et al. Meta-strategy learning in physical problem solving: the effect of embodied experience. bioRxiv (2021).PDF icon 2021.07.08.451333v2.full_.pdf (3.05 MB)
Sosa, F. A., Ullman, T., Tenenbaum, J. B., Gershman, S. J. & Gerstenberg, T. Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology. Cognition 217, 104890 (2021).
Yaari, A. Uri et al. Multi-resolution modeling of a discrete stochastic process identifies causes of cancer. International Conference on Learning Representations (2021). at <>
Marques, T., Schrimpf, M. & DiCarlo, J. J. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv (2021).PDF icon 2021.03.01.433495v2.full_.pdf (3.23 MB)
Tomov, M. S., Schulz, E. & Gershman, S. J. Multi-task reinforcement learning in humans. Nature Human Behaviour (2021). doi:10.1038/s41562-020-01035-y
Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences 118, e2105646118 (2021).
Houlihan, S. Dae, Tenenbaum, J. B. & Saxe, R. The Neural Basis of Mentalizing: Linking Models of Theory of Mind and Measures of Human Brain Activity. 209 - 235 (Springer International Publishing, 2021). doi:10.1007/978-3-030-51890-510.1007/978-3-030-51890-5_11