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2020
Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B. & Cushman, F. A. The logic of universalization guides moral judgment. Proceedings of the National Academy of Sciences (PNAS) 202014505 (2020). doi:10.1073/pnas.2014505117
Ben-Yosef, G., Kreiman, G. & Ullman, S. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (2020). doi:10.1016/j.cognition.2020.104263
Lotter, W., Kreiman, G. & Cox, D. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).
Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (2020).PDF icon 1805.10734.pdf (9.59 MB)
Sheskin, M. et al. Online Developmental Science to Foster Innovation, Access, and Impact. Trends in Cognitive Sciences 24, 675 - 678 (2020).
Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://openreview.net/forum?id=_bokm801zhx>PDF icon phase_physically_grounded_abstract_social_events_for_machine_social_perception.pdf (2.49 MB)
Zhang, M., Tseng, C. & Kreiman, G. Putting visual object recognition in context. CVPR 2020 (2020).PDF icon gk7876.pdf (3.12 MB)
Richardson, H. et al. Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder. Cortex 125, 12 - 29 (2020).
Richardson, H. et al. Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder. Cortex 125, 12 - 29 (2020).
Isik, L., Mynick, A., Pantazis, D. & Kanwisher, N. The speed of human social interaction perception. NeuroImage 116844 (2020). doi:10.1016/j.neuroimage.2020.116844
Gen, C. et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954>PDF icon 2007.04954.pdf (7.06 MB)
Gen, C. et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954>PDF icon 2007.04954.pdf (7.06 MB)
Schwartz, J. et al. ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>
Schwartz, J. et al. ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
Ben-Yosef, G., Kreiman, G. & Ullman, S. What can human minimal videos tell us about dynamic recognition models?. International Conference on Learning Representations (ICLR 2020) (2020). at <https://baicsworkshop.github.io/pdf/BAICS_1.pdf>PDF icon Authors' final version (516.09 KB)
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks . Conference on Cognitive Computational Neuroscience (2020).
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks . Conference on Cognitive Computational Neuroscience (2020).
Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).PDF icon gk7791.pdf (2.39 MB)
2019
Jozwik, K. M., Lee, H., Kanwisher, N. & DiCarlo, J. J. Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
Kryven, M., Niemi, L., Paul, L. & Tenenbaum, J. B. Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Kuo, Y. - L., Katz, B. & Barbu, A. Deep Compositional Robotic Planners that Follow Natural Language Commands. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), (2019). at <https://vigilworkshop.github.io/>

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