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Found 912 results
Author [ Title(Desc)] Type Year
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C
Hartshorne, J. K. The causes and consequences explicit in verbs. Language, Cognition and Neuroscience 30, 716-734 (2015).
Poggio, T. & Magrini, M. Cervelli menti algoritmi. 272 (Sperling & Kupfer, 2023). at <https://www.sperling.it/libri/cervelli-menti-algoritmi-marco-magrini>
Magid, R., Yan, P., Siegel, M., Tenenbaum, J. B. & Schulz, L. Changing minds: Children’s inferences about third party belief revision. Developmental Science e12553 (2017). doi:10.1111/desc.12553PDF icon Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
Winston, P. Henry & Holmes, D. Character-building stories. Advances in Cognitive Systems (2017).
Cohen, M. A., Ostrand, C., Frontero, N. & Pham, P. - N. Characterizing a snapshot of perceptual experience. Journal of Experimental Psychology: General (2021). doi:10.1037/xge0000864
Kar, K., Schrimpf, M., Schmidt, K. & 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 21, (2021).
Spokes, A. C. & Spelke, E. S. Children’s Expectations and Understanding of Kinship as a Social Category. Frontiers in Psychology 7, 1664-1078 (2016).
Jara-Ettinger, J., Gweon, H., Tenenbaum, J. B. & Schulz, L. Children’s understanding of the costs and rewards underlying rational action. Cognition 140, 14–23 (2015).PDF icon CM_inPress.pdf (438.5 KB)
Jara-Ettinger, J., Floyd, S., Tenenbaum, J. B. & Schulz, L. Children understand that agents maximize expected utilities. Journal of Experimental Psychology: General 146, 1574 - 1585 (2017).PDF icon ExpectedUtilities_Final.pdf (950.09 KB)
Dillon, M. R., Pires, A. C., Hyde, D. C. & Spelke, E. S. Children's expectations about training the approximate number system. British Journal of Developmental Psychology 33, (2015).
Kryven, M., Niemi, L., Paul, L. & Tenenbaum, J. B. Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
Kanwisher, N., Gupta, P. & Dobs, K. CNNs reveal the computational implausibility of the expertise hypothesis. iScience 26, 105976 (2023).
Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).File cnpkg.tar (50 KB)
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Spelke, E. S., Sternberg, R. J., Fiske, S. T. & Foss, D. J. Scientists Making a Difference: One Hundred Eminent Behavioral and Brain Scientists Talk about Their Most Important Contributions (Cambridge University Press, 2016).
Zheng, J. et al. Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. bioRxiv (2021).
Xiang, Y., Vélez, N. & Gershman, S. J. Collaborative decision making is grounded in representations of other people’s competence and effort. Journal of Experimental Psychology: General 152, 1565 - 1579 (2023).
Lafer-Sousa, R., Conway, B. R. & Kanwisher, N. Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques. Journal of Neuroscience 36, 1682 - 1697 (2016).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Baidya, A., Dapello, J., DiCarlo, J. J. & Marques, T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/ScheduleMultitrack?event=41268>
Schulz, E., Quiroga, F. & Gershman, S. J. Communicating Compositional Patterns. Open Mind 4, 25 - 39 (2020).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Social & Affective Neuroscience Society (SANS) (2018). at <http://www.socialaffectiveneuro.org/conferences.html>
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Organization for Computational Neurosciences - CNS 2018 (2018). at <http://www.cnsorg.org/cns-2018>

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