Publication

Export 783 results:
2022
Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>
Dobs, K., Martinez, 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
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. & 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)
Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. arXiv (2022). doi:10.48550/arXiv.2201.11665PDF icon 2201.11665.pdf (735.07 KB)
Becker, L. A. et al. Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx. Frontiers in Pharmacology 12, (2022).
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
Harrington, A. & Deza, A. Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks. International Conference on Learning Representations (ICLR) (2022). at <https://openreview.net/forum?id=yeP_zx9vqNm>
McPherson, M. J., Grace, R. C. & McDermott, J. H. Harmonicity aids hearing in noise. Attention, Perception, & Psychophysics (2022). doi:10.3758/s13414-021-02376-0
Galanti, T. & Galanti, L. On the Implicit Bias Towards Minimal Depth of Deep Neural Networks. arXiv (2022). at <https://arxiv.org/abs/2202.09028>PDF icon 2202.09028.pdf (2 MB)
Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).PDF icon CBMM-Memo-133.pdf (1.68 MB)
Berrios, W. & Deza, A. Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4. BrainScore Workshop at COSYNE (2022). at <https://openreview.net/pdf?id=SOulrWP-Xb5>
Banburski, A. & Rangamani, A. Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay. IEEE International Conference on Acoustics, Speech and Signal Processing (2022).
Norman-Haignere, S. V. et al. A neural population selective for song in human auditory cortex. Current Biology (2022). doi:10.1016/j.cub.2022.01.069PDF icon mmc2.pdf (9.52 MB)
Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).
Casper, S., Nadeau, M. & Kreiman, G. One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features. arXiv (2022). doi:10.48550/arXiv.2110.03605PDF icon 2110.03605.pdf (6.7 MB)
Gupte, A., Banburski, A. & Poggio, T. PCA as a defense against some adversaries. (2022).PDF icon CBMM-Memo-135.pdf (2.58 MB)
Galanti, T. & Poggio, T. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. (2022).PDF icon Implicit Rank Regularization.pdf (1.42 MB)
Xiao, Y. et al. Task-specific neural processes underlying conflict resolution during cognitive control. BioRxiv (2022). doi:10.1101/2022.01.16.476535 PDF icon 2022.01.16.476535v1.full_.pdf (22.96 MB)
Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).PDF icon CBMM-Memo-119.pdf (31.08 MB)
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).PDF icon CBMM-Memo-132.pdf (1.15 MB)
Yamada, M., D'Amario, V., Takemoto, K., Boix, X. & Sasaki, T. Transformer Module Networks for Systematic Generalization in Visual Question Answering. (2022).PDF icon CBMM-Memo-121.pdf (1.06 MB)

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