All Publications

2022

E. M. Meyers, “NeuroDecodeR: A package for neural decoding analyses in R”, bioRxiv, 2022.
CBMM Funded
T. Pouncy and Gershman, S. J., “Inductive biases in theory-based reinforcement learning”, Cognitive Psychology, vol. 138, p. 101509, 2022.
CBMM Funded
Y. Tazi, Berger, M., and Freiwald, W. A., “Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models”, arXiv, 2022.
CBMM Funded
H. S. Wirtshafter and Wilson, M. A., “Artificial intelligence insights into hippocampal processing”, Frontiers in Computational Neuroscience, vol. 16, 2022.
CBMM Related
M. Zhang, Armendariz, M., Xiao, W., Rose, O., Bendtz, K., Livingstone, M., Ponce, C., and Kreiman, G., “Look twice: A generalist computational model predicts return fixations across tasks and species”, PLOS Computational Biology, vol. 18, no. 11, p. e1010654, 2022.PDF icon journal.pcbi_.1010654.pdf (4.51 MB)
CBMM Funded
CBMM Memo No.
138
T. A. Poggio, “How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse”. 2022.PDF icon v1.0 (984.15 KB)PDF icon Theoretical_Framework__How_Deep_Nets_May_Work.pdf (1.15 MB)
CBMM Funded
C. Xu, Yang, S., Galanti, T., Wu, B., Yue, X., Zhai, B., Zhan, W., Vajda, P., Keutzer, K., and Tomizuka, M., Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models, vol. 13697. Cham: Springer Nature Switzerland, 2022, pp. 638 - 656.
CBMM Funded
A. Marliawaty Bagus, Marques, T., Sanghavi, S., DiCarlo, J. J., and Schrimpf, M., “Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units”, in NeurIPS, 2022.
CBMM Related
S. Casper, Nadeau, M., Hadfield-Menell, D., and Kreiman, G., “Robust Feature-Level Adversaries are Interpretability Tools”, NeurIPS. New Orleans, Louisiana, 2022.PDF icon 8789_robust_feature_level_adversari.pdf (3.79 MB)
CBMM Funded
M. Khosla, N. Murty, A. Ratan, and Kanwisher, N., “A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition”, Current Biology, vol. 32, no. 19, pp. 4159 - 4171.e9, 2022.
CBMM Related
M. Letizia, Losapio, G., Rando, M., Grosso, G., Wulzer, A., Pierini, M., Zanetti, M., and Rosasco, L., “Learning new physics efficiently with nonparametric methodsAbstract”, The European Physical Journal C, vol. 82, no. 10, 2022.
CBMM Related
S. Liu, Pepe, B., Kumar, M. Ganesh, Ullman, T. D., Tenenbaum, J. B., and Spelke, E. S., “Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract”, Open Mind, vol. 6, pp. 211 - 231, 2022.
CBMM Funded
F. Anselmi and Poggio, T., “Representation Learning in Sensory Cortex: a theory”, IEEE Access, pp. 1 - 1, 2022.PDF icon Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
CBMM Funded
F. Montagna, Noceti, N., Rosasco, L., Zhang, K., and Locatello, F., “Scalable Causal Discovery with Score Matching”, NeurIPS 2022. 2022.
CBMM Related
K. Ellis, Albright, A., Solar-Lezama, A., Tenenbaum, J. B., and O’Donnell, T. J., “Synthesizing theories of human language with Bayesian program inductionAbstract”, Nature Communications, vol. 13, no. 1, 2022.PDF icon s41467-022-32012-w.pdf (2.19 MB)
CBMM Funded
V. Izard, Pica, P., and Spelke, E. S., “Visual foundations of Euclidean geometry”, Cognitive Psychology, vol. 136, p. 101494, 2022.
CBMM Related
E. S. Spelke, What Babies KnowAbstractCore KnowledgeAbstract, 1st ed. Oxford University PressNew York, 2022, pp. 190 - C5.T1.
CBMM Related
B. M. Woo and Spelke, E. S., “Toddlers’ social evaluations of agents who act on false beliefs”, Developmental Science, vol. 26, no. 2, 2022.
CBMM Funded
CBMM Memo No.
137
A. Rangamani and Xie, Y., “Understanding the Role of Recurrent Connections in Assembly Calculus”. 2022.PDF icon CBMM-Memo-137.pdf (1.49 MB)
CBMM Funded
CBMM Memo No.
136
Y. Han, Poggio, T., and Cheung, B., “System identification of neural systems: If we got it right, would we know?”. 2022.PDF icon CBMM-Memo-136.pdf (1.75 MB)
CBMM Funded
J. Dapello, Kar, K., Schrimpf, M., Geary, R., Ferguson, M., Cox, D. D., and DiCarlo, J. J., “Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness”, bioRxiv, 2022.
CBMM Related
G. Dellaferrera and 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, vol. 162. pp. 4937-4955, 2022.PDF icon dellaferrera22a.pdf (909.91 KB)
CBMM Funded
S. D. Houlihan, Ong, D., Cusimano, M., and Saxe, R., “Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind”, Proceedings of the 44th Annual Conference of the Cognitive Science Society, vol. 44. Toronto, CA, pp. 854-861, 2022.PDF icon Houlihan 2022 Proceedings of the 44th Annual Conference of the Cognitive Science Society.pdf (687.98 KB)
CBMM Funded
N. Shaham, Chandra, J., Kreiman, G., and Sompolinsky, H., “Stochastic consolidation of lifelong memoryAbstract”, Scientific Reports, vol. 12, no. 1, 2022.PDF icon s41598-022-16407-9.pdf (2.54 MB)
CBMM Funded
N. N. Gjata, Ullman, T. D., Spelke, E. S., and Liu, S., “What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger”, Cognitive Science, vol. 46, no. 7, 2022.
CBMM Funded

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