All Publications

2022

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. Dae 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 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
C. Guo, Lee, M. J., Leclerc, G., Dapello, J., Rao, Y., Madry, A., and DiCarlo, J. J., “Adversarially trained neural representations may already be as robust as corresponding biological neural representations”, arXiv, 2022.
CBMM Related
K. Kar, “A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism”, The Journal of Neuroscience, pp. JN-RM-2229-21, 2022.
CBMM Related
P. Nilchian, Wilson, M. A., and Sanders, H., “Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments”, The Journal of Neuroscience, vol. 42, no. 26, pp. 5268 - 5280, 2022.PDF icon 5268.full_.pdf (2.97 MB)
CBMM Funded
M. A. Sherman, Yaari, A. U., Priebe, O., Dietlein, F., Loh, P. - R., and Berger, B., “Genome-wide mapping of somatic mutation rates uncovers drivers of cancerAbstract”, Nature Biotechnology, vol. 40, no. 11, pp. 1634 - 1643, 2022.
CBMM Related
R. T. Pramod, Cohen, M. A., Tenenbaum, J. B., and Kanwisher, N., “Invariant representation of physical stability in the human brain”, eLife, vol. 11, 2022.
CBMM Related
A. Banburski and Rangamani, A., “Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay”, in IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2022.
CBMM Funded
R. Tejwani, Kuo, Y. - L., Shu, T., Stankovits, B., Gutfreund, D., Tenenbaum, J. B., Katz, B., and Barbu, A., “Incorporating Rich Social Interactions Into MDPs”, in 2022 IEEE International Conference on Robotics and Automation (ICRA)2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 2022.
CBMM Funded
A. J. Thomas, Saxe, R., and Spelke, E. S., “Infants infer potential social partners by observing the interactions of their parent with unknown others”, Proceedings of the National Academy of Sciences, vol. 119, no. 32, 2022.PDF icon pnas.2121390119.pdf (1.43 MB)
CBMM Funded
M. A. Gartstein, D. Seamon, E., Mattera, J. A., Enlow, M. Bosquet, Wright, R. J., Perez-Edgar, K., Buss, K. A., LoBue, V., Bell, M. Ann, Goodman, S. H., Spieker, S., Bridgett, D. J., Salisbury, A. L., Gunnar, M. R., Mliner, S. B., Muzik, M., Stifter, C. A., Planalp, E. M., Mehr, S. A., Spelke, E. S., Lukowski, A. F., Groh, A. M., Lickenbrock, D. M., Santelli, R., Schudlich, T. Du Rocher, Anzman-Frasca, S., Thrasher, C., Diaz, A., Dayton, C., Moding, K. J., and Jordan, E. M., “Using machine learning to understand age and gender classification based on infant temperament”, PLOS ONE, vol. 17, no. 4, p. e0266026, 2022.
CBMM Related
N. Zaslavsky, Garvin, K., Kemp, C., Tishby, N., and Regier, T., “The evolution of color naming reflects pressure for efficiency: Evidence from the recent pastAbstract”, Journal of Language Evolution, 2022.
CBMM Related
K. Dobs, Martinez-Trujillo, J., Kell, A. J. E., and Kanwisher, N., “Brain-like functional specialization emerges spontaneously in deep neural networks”, Science Advances, vol. 8, no. 11, 2022.
CBMM Funded
CBMM Memo No.
134
T. Galanti and Poggio, T., “SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks”. 2022.PDF icon Implicit Rank Minimization.pdf (1.76 MB)
CBMM Funded
T. Galanti and Galanti, L., “On the Implicit Bias Towards Minimal Depth of Deep Neural Networks”, arXiv, 2022.PDF icon 2202.09028.pdf (2 MB)
CBMM Funded
F. S. Kamps, Richardson, H., N. Murty, A. Ratan, Kanwisher, N., and Saxe, R., “Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years”, Human Brain Mapping, 2022.
CBMM Related
Y. Li, Wang, Y. Q., Boger, T., Smith, K. A., Gershman, S. J., and Ullman, T., “An approximate representation of objects underlies physical reasoning”, psyArXiv, 2022.
CBMM Related
J. Zheng, Schjetnan, A. G. P., Yebra, M., Gomes, B. A., Mosher, C. P., Kalia, S. K., Valiante, T. A., Mamelak, A. N., Kreiman, G., and Rutishauser, U., “Neurons detect cognitive boundaries to structure episodic memories in humans”, Nature Neuroscience, vol. 25, no. 3, pp. 358 - 368, 2022.
CBMM Funded
CBMM Memo No.
121
M. Yamada, D'Amario, V., Takemoto, K., Boix, X., and Sasaki, T., “Transformer Module Networks for Systematic Generalization in Visual Question Answering”. 2022.PDF icon CBMM-Memo-121.pdf (1.06 MB)PDF icon version 2 (3/22/2023) (1.33 MB)
CBMM Funded
M. Armendariz, Xiao, W., Vinken, K., and Kreiman, G., “Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions”, Cognitive Neuropsychology, pp. 1 - 3, 2022.
CBMM Funded
A. Bardon, Xiao, W., Ponce, C. R., Livingstone, M. S., and Kreiman, G., “Face neurons encode nonsemantic features”, Proceedings of the National Academy of Sciences, vol. 119, no. 16, 2022.
CBMM Funded
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations”, Nature Machine Intelligence, vol. 4, no. 2, pp. 146 - 153, 2022.
CBMM Funded

Pages