Publication

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2023
Gershman, S. J. & Cikara, M. Structure learning principles of stereotype change. Psychonomic Bulletin & Review 30, 1273 - 1293 (2023).
Duan, A. et al. A structured prediction approach for robot imitation learning. The International Journal of Robotics Research 43, 113 - 133 (2023).
Yu, C., Burgess, N., Sahani, M. & Gershman, S. J. Successor-Predecessor Intrinsic Exploration. Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/e6f2b968c4ee8ba260cd7077e39590dd-Abstract-Conference.html>
Han, Y., Poggio, T. & Cheung, B. System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444 (2023).PDF icon han23d.pdf (797.48 KB)
Vélez, N., Chen, A. M., Burke, T., Cushman, F. A. & Gershman, S. J. Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
Kanwisher, N., Khosla, M. & Dobs, K. Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences 46, 240 - 254 (2023).
Subramaniam, V. et al. Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>
Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).
2022
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).
Yaari, A. et al. The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (2022).
Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).
Nilchian, P., Wilson, M. A. & Sanders, H. Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments. The Journal of Neuroscience 42, 5268 - 5280 (2022).PDF icon 5268.full_.pdf (2.97 MB)
Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>
Wirtshafter, H. S. & Wilson, M. A. Artificial intelligence insights into hippocampal processing. Frontiers in Computational Neuroscience 16, (2022).
Dobs, K., Martinez-Trujillo, J., Kell, A. J. E. & Kanwisher, N. Brain-like functional specialization emerges spontaneously in deep neural networks. Science Advances 8, (2022).
Kar, K. A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism. The Journal of Neuroscience JN-RM-2229-21 (2022). doi:10.1523/JNEUROSCI.2229-21.2022
Liu, S. et al. Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).
Francl, A. & McDermott, J. H. Deep neural network models of sound localization reveal how perception is adapted to real-world environments. Nature Human Behavior 6, 111–133 (2022).PDF icon s41562-021-01244-z.pdf (7.22 MB)
Armendariz, M., Xiao, W., Vinken, K. & Kreiman, G. Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588
Thomas, A. J., Woo, B., Nettle, D., Spelke, E. S. & Saxe, R. Early concepts of intimacy: Young humans use saliva sharing to infer close relationships. Science 375, 311 - 315 (2022).
Sikarwar, A. & Kreiman, G. On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. arXiv (2022). doi:10.48550/arXiv.2201.03965PDF icon On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB)
Woo, B. & Spelke, E. Eight-Month-Old Infants’ Social Evaluations of Agents Who Act on False Beliefs. Proceedings of the Annual Meeting of the Cognitive Science Society 44, (2022).
Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).PDF icon dellaferrera22a.pdf (909.91 KB)
Becker, L. A. et al. Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx. Frontiers in Pharmacology 12, (2022).
Zaslavsky, N., Garvin, K., Kemp, C., Tishby, N. & Regier, T. The evolution of color naming reflects pressure for efficiency: Evidence from the recent pastAbstract. Journal of Language Evolution (2022). doi:10.1093/jole/lzac001

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