All Publications

2022

A. Francl and McDermott, J. H., “Deep neural network models of sound localization reveal how perception is adapted to real-world environments”, Nature Human Behavior, vol. 6, pp. 111–133 , 2022.PDF icon s41562-021-01244-z.pdf (7.22 MB)
CBMM Funded
M. J. McPherson, Grace, R. C., and McDermott, J. H., “Harmonicity aids hearing in noise”, Attention, Perception, & Psychophysics, 2022.
CBMM Related
A. Sikarwar and Kreiman, G., “On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering”, arXiv, 2022.PDF icon On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB)
CBMM Funded
A. J. Thomas, Woo, B., Nettle, D., Spelke, E. S., and Saxe, R., “Early concepts of intimacy: Young humans use saliva sharing to infer close relationships”, Science, vol. 375, no. 6578, pp. 311 - 315, 2022.
CBMM Funded
Y. Xiao, Chou, C. - C., Cosgrove, G. Rees, Crone, N. E., Stone, S., Madsen, J. R., Reucroft, I., Shih, Y. - C., Weisholtz, D., Yu, H. - Y., Anderson, W. S., and Kreiman, G., “Task-specific neural processes underlying conflict resolution during cognitive control”, BioRxiv, 2022.PDF icon 2022.01.16.476535v1.full_.pdf (22.96 MB)
CBMM Funded
CBMM Memo No.
135
A. Gupte, Banburski, A., and Poggio, T., “PCA as a defense against some adversaries”. 2022.PDF icon CBMM-Memo-135.pdf (2.58 MB)
CBMM Funded
CBMM Memo No.
133
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”. 2022.PDF icon CBMM-Memo-133.pdf (1.68 MB)
CBMM Funded
CBMM Memo No.
132
Y. - L. Kuo, Huang, X., Barbu, A., McGill, S. G., Katz, B., Leonard, J. J., and Rosman, G., “Trajectory Prediction with Linguistic Representations”. 2022.PDF icon CBMM-Memo-132.pdf (1.15 MB)
CBMM Funded
M. Bendini and Devercelli, A., Eds., Quality Early Learning: Nurturing Children's Potential. The World Bank, 2022.
CBMM Related
W. Berrios and Deza, A., “Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4”, in BrainScore Workshop at COSYNE, 2022.
CBMM Funded
E. Cheng, Kuo, Y. - L., Correa, J., Katz, B., Cases, I., and Barbu, A., “Quantifying the Emergence of Symbolic Communication”, CogSci, 2022.
CBMM Related
E. Cheng, Kuo, Y. - L., Cases, I., Katz, B., and Barbu, A., “Spontaneous sign emergence in humans and machines through an embodied communication game”, in JCoLE Workshop, 2022.
CBMM Related
A. Harrington and Deza, A., “Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, International Conference on Learning Representations (ICLR). 2022.
CBMM Funded
Y. - L. Kuo, Huang, X., Barbu, A., McGill, S. G., Katz, B., Leonard, J. J., and Rosman, G., “Trajectory Prediction with Linguistic Representations”, in 2022 IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 2022.
CBMM Funded
B. Woo and 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, vol. 44, 2022.
CBMM Funded
A. Yaari, DeWitt, J., Hu, H., Stankovits, B., Felshin, S., Berzak, Y., Aparicio, H., Katz, B., Cases, I., and Barbu, A., “The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank”, in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022.
CBMM Related

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

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