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2019
Araya-Polo, M., Adler, A., Farris, S. & Jennings, J. Deep Learning: Algorithms and Applications (SPRINGER-VERLAG, 2019).
Serrino, J., Kleiman-Weiner, M., Parkes, D. C. & Tenenabum, J. B. Finding Friend and Foe in Multi-Agent Games. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon Max KW paper.pdf (928.96 KB)
Gershman, S. J. The Generative Adversarial Brain. Frontiers in Artificial Intelligence 2, (2019).
Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260PDF icon CBMM-Memo-101.pdf (12.78 MB)
Leonard, J. A., Garcia, A. & Schulz, L. E. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305
Idiart, M. A. P., Villavicencio, A., Katz, B., Rennó-Costa, C. & Lisman, J. How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (2019).
Dobs, K., Isik, L., Pantazis, D. & Kanwisher, N. How face perception unfolds over time. Nature Communications 10, (2019).
Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).
Harrod, J., Purdon, P. L., Brown, E. N. & Flores, F. J. Identification of vigilance states in freely behaving animals using thalamocortical activity and Deep Belief networks. Society for Neuroscience (2019).
McWalter, R. & McDermott, J. H. Illusory sound texture reveals multi-second statistical completion in auditory scene analysis. Nature Communications 10, (2019).
Pagliana, N. & Rosasco, L. Implicit Regularization of Accelerated Methods in Hilbert Spaces. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9591-implicit-regularization-of-accelerated-methods-in-hilbert-spaces.pdf (451.14 KB)
Betta, I. Dalla et al. In silico modeling of temporally interfering electric fields for deep brain stimulation . Society for Neuroscience (2019).
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs. Biological Psychiatry 85, 425 - 433 (2019).
Yildirim, I., Wu, J., Kanwisher, N. & Tenenbaum, J. B. An integrative computational architecture for object-driven cortex. Current Opinion in Neurobiology 55, 73 - 81 (2019).
Kell, A. J. E. & McDermott, J. H. Invariance to background noise as a signature of non-primary auditory cortex. Nature Communications 10, (2019).
Schwettmann, S., Tenenbaum, J. B. & Kanwisher, N. Invariant representations of mass in the human brain. eLife 8, (2019).
McCoy, J. & Ullman, T. Judgments of effort for magical violations of intuitive physics. PLOS ONE 14, e0217513 (2019).
Calero, C. I., Shalom, D. E., Spelke, E. S. & Sigman, M. Language, gesture, and judgment: Children’s paths to abstract geometry. Journal of Experimental Child Psychology 177, 70 - 85 (2019).
Jozwik, K. M., Lee, M., Marques, T., Schrimpf, M. & Bashivan, P. Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844
Ross, C., Berzak, Y., Katz, B. & Barbu, A. Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).
Anzellotti, S., Houlihan, S. Dae, Liburd, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2019). doi:10.1037/emo0000685
Marques, T. & DiCarlo, J. J. A meta-analysis of ANNs as models of primate V1 . Bernstein (2019).
Feather, J., Durango, A., Gonzalez, R. & McDermott, J. H. Metamers of neural networks reveal divergence from human perceptual systems. NIPS 2019 (2019). at <https://papers.nips.cc/paper/9198-metamers-of-neural-networks-reveal-divergence-from-human-perceptual-systems>PDF icon Feather_etal_2019_NeurIPS_metamers.pdf (4.7 MB)
Srivastava, S., Ben-Yosef, G. & Boix, X. Minimal images in deep neural networks: Fragile Object Recognition in Natural Images. International Conference on Learning Representations (ICLR) (2019). at <https://arxiv.org/pdf/1902.03227.pdf>

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