All Publications

2021

Y. Xiang, Graeber, T., Enke, B., and Gershman, S. J., “Confidence and central tendency in perceptual judgment”, Attention, Perception, & Psychophysics, vol. 83, no. 7, pp. 3024 - 3034, 2021.
CBMM Funded
D. S. Weisholtz, Kreiman, G., Silbersweig, D. A., Stern, E., Cha, B., and Butler, T., “Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract”, Social Cognitive and Affective Neuroscience, 2021.
CBMM Related
A. Baidya, Dapello, J., DiCarlo, J. J., and Marques, T., “Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs”, in NeurIPS 2021, 2021.
CBMM Funded
J. Gant, Banburski, A., Deza, A., and Poggio, T., “Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN”, NeurIPS 2021. 2021.
CBMM Funded
S. Kant Gupta, Zhang, M., WU, C. H. I. A. - C. H. I. E. N., Wolfe, J., and Kreiman, G., “Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases”, NeurIPS 2021. 2021.PDF icon gk8091.pdf (2.47 MB)
CBMM Funded
A. Ratan N. Murty, Bashivan, P., Abate, A., DiCarlo, J. J., and Kanwisher, N., “Computational models of category-selective brain regions enable high-throughput tests of selectivity”, Nature Communications, vol. 12, no. 1, 2021.PDF icon s41467-021-25409-6.pdf (6.47 MB)
CBMM Funded
M. R. Saddler, Gonzalez, R., and McDermott, J. H., “Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception”, Nature Communications, vol. 12, no. 1, 2021.PDF icon s41467-021-27366-6.pdf (5.25 MB)
CBMM Related
CBMM Memo No.
120
S. Ullman, Assif, L., Strugatski, A., Vatashsky, B. - Z., Levi, H., Netanyahu, A., and Yaari, A. Uri, “Image interpretation by iterative bottom-up top- down processing”. 2021.PDF icon CBMM-Memo-120.pdf (2.83 MB)
CBMM Funded
I. Bass, Smith, K. A., Bonawitz, E., and Ullman, T., “Partial Mental Simulation Explains Fallacies in Physical Reasoning”, psyArXiv, 2021.
CBMM Related
H. L. Kosakowski, Cohen, M. A., Takahashi, A., Keil, B., Kanwisher, N., and Saxe, R., “Selective responses to faces, scenes, and bodies in the ventral visual pathway of infants”, Current Biology, vol. 32, 2021.
CBMM Funded
M. Schrimpf, Blank, I. Asher, Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., and Fedorenko, E., “The neural architecture of language: Integrative modeling converges on predictive processing”, Proceedings of the National Academy of Sciences, vol. 118, no. 45, p. e2105646118, 2021.
CBMM Funded
C. Bongiorno, Zhou, Y., Kryven, M., Theurel, D., Rizzo, A., Santi, P., Tenenbaum, J. B., and Ratti, C., “Vector-based pedestrian navigation in cities”, Nature Computational Science, vol. 1, no. 10, pp. 678 - 685, 2021.PDF icon s43588-021-00130-y.pdf (1.96 MB)
CBMM Funded
CBMM Memo No.
118
T. Poggio, “From Marr’s Vision to the Problem of Human Intelligence”. 2021.PDF icon CBMM-Memo-118.pdf (362.19 KB)
CBMM Funded
K. Kar, Schrimpf, M., Schmidt, K., and DiCarlo, J. J., “ Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior”, Journal of Vision, vol. 21, 2021.
CBMM Related
M. Kryven, Ullman, T. D., Cowan, W., and Tenenbaum, J. B., “Plans or Outcomes: How Do We Attribute Intelligence to Others?”, Cognitive Science, vol. 45, no. 9, 2021.
CBMM Related
A. Uri Yaari, Sherman, M., Priebe, O. Clarke, Loh, P. - R., Katz, B., Barbu, A., and Berger, B., “Multi-resolution modeling of a discrete stochastic process identifies causes of cancer”, in International Conference on Learning Representations, 2021.
CBMM Funded
K. Allen, Smith, K. A., Bird, L., Tenenbaum, J. B., Makin, T., and Cowie, D., “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)
CBMM Funded
P. Bomatter, Zhang, M., Karev, D., Madan, S., Tseng, C., and Kreiman, G., “When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes”, International Conference on Computer Vision (ICCV) . 2021.PDF icon Bomatter_When_Pigs_Fly_Contextual_Reasoning_in_Synthetic_and_Natural_Scenes_ICCV_2021_paper.pdf (3.24 MB)
CBMM Funded
T. Marques, Schrimpf, M., and 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)
CBMM Funded
I. Palmer, Rouditchenko, A., Barbu, A., Katz, B., and Glass, J., “Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset”, in Interspeech 2021, ISCA, 2021.
CBMM Funded
J. Wang, Tao, A., Anderson, W. S., Madsen, J. R., and Kreiman, G., “Mesoscopic physiological interactions in the human brain reveal small-world properties”, Cell Reports, vol. 36, no. 8, p. 109585, 2021.
CBMM Funded
Y. - L. Kuo, Katz, B., and Barbu, A., “Compositional RL Agents That Follow Language Commands in Temporal Logic”, Frontiers in Robotics and AI, vol. 8, 2021.PDF icon frobt-08-689550.pdf (1.57 MB)
CBMM Funded
J. Hu, Zaslavsky, N., and Levy, R., “Competition from novel features drives scalar inferences in reference games”, Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 43. 2021.
CBMM Related
S. M. Landi, Viswanathan, P., Serene, S., and Freiwald, W. A., “A fast link between face perception and memory in the temporal pole”, Science, p. eabi6671, 2021.
CBMM Funded
T. Shu, Bhandwaldar, A., Gan, C., Smith, K. A., Liu, S., Gutfreund, D., Spelke, E. S., Tenenbaum, J. B., and Ullman, T. D., “ AGENT: A Benchmark for Core Psychological Reasoning”, Proceedings of the 38th International Conference on Machine Learning. 2021.
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