Publication

Found 60 results
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2018
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65 - 84 (2018).
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65-84 (2018).PDF icon Full interpretation of minimal images.pdf (4.55 MB)
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. Interface Focus 8, 20180020 (2018).
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. Proceedings of the Royal Society: Interface Focus (2018).PDF icon 2018-BenYosef_Ullman-Image_interpretation_above_and_below the object_level.pdf (3.26 MB)
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. (2018).PDF icon CBMM-Memo-089.pdf (2.06 MB)
Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).
McCoy, J. P. & Ullman, T. D. A Minimal Turing Test. Journal of Experimental Social Psychology 79, 1 - 8 (2018).
Owaki, T. et al. Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).
Owaki, T. et al. Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).
Ben-Yosef, G., Kreiman, G. & Ullman, S. Spatiotemporal interpretation features in the recognition of dynamic images. (2018).PDF icon CBMM-Memo-094.pdf (1.21 MB)Package icon CBMM-Memo-094-dynamic-figures.zip (1.8 MB)File fig1.ppsx (147.67 KB)File fig2.ppsx (419.72 KB)File fig4.ppsx (673.41 KB)File figS1.ppsx (587.88 KB)File figS2.ppsx (281.56 KB)
2019
Ullman, T. D. et al. Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. Cognitive Science Society (2019). at <https://mindmodeling.org/cogsci2019/papers/0506/index.html>PDF icon Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
McCoy, J. P. & Ullman, T. Judgments of effort for magical violations of intuitive physics. PLOS ONE 14, e0217513 (2019).
Ullman, S., Dorfman, N. & Harari, D. A model for discovering ‘containment’ relations. Cognition 183, 67 - 81 (2019).
Smith, K. A. et al. Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019). at <http: //physadept.csail.mit.edu/>PDF icon ADEPT_NeurIPS.pdf (11.07 MB)
Liu, S., McCoy, J. P. & Ullman, T. D. People's perceptions of others’ risk preferences. Cognitive Science Society (2019).PDF icon risk_cogsci_2019_final.pdf (899.8 KB)
Jacoby, N. et al. Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing. Current Biology (2019). doi:10.1016/j.cub.2019.08.020
Ullman, S. Using neuroscience to develop artificial intelligence. Science 363, 692 - 693 (2019).
2020
Udrescu, S. - M. et al. AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020).PDF icon 2006.10782.pdf (2.62 MB)
Ullman, T. D. & Tenenbaum, J. B. Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology 2, 533 - 558 (2020).
Smith, K. A. et al. The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 (Denison, S., Mack, M., Xu, Y. & Armstrong, B. C.) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>
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
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)
2021
Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).
Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).PDF icon CBMM-Memo-120.pdf (2.83 MB)

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