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2021
Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).
Allen, K. et al. Meta-strategy learning in physical problem solving: the effect of embodied experience. bioRxiv (2021).PDF icon 2021.07.08.451333v2.full_.pdf (3.05 MB)
Sosa, F. A., Ullman, T., Tenenbaum, J. B., Gershman, S. J. & Gerstenberg, T. Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology. Cognition 217, 104890 (2021).
Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences 118, e2105646118 (2021).
Houlihan, S. Dae, Tenenbaum, J. B. & Saxe, R. The Neural Basis of Mentalizing: Linking Models of Theory of Mind and Measures of Human Brain Activity. 209 - 235 (Springer International Publishing, 2021). doi:10.1007/978-3-030-51890-510.1007/978-3-030-51890-5_11
Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. (2021).PDF icon CBMM-Memo-123.pdf (3.08 MB)
Kryven, M., Ullman, T. D., Cowan, W. & Tenenbaum, J. B. Plans or Outcomes: How Do We Attribute Intelligence to Others?. Cognitive Science 45, (2021).
Mao, J. et al. Temporal and Object Quantification Networks. Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (Zhou, Z. - H.) (2021). doi:10.24963/ijcai.2021/386PDF icon 0386.pdf (472.5 KB)
Du, Y., Smith, K. A., Ullman, T., Tenenbaum, J. B. & Wu, J. Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>
2020
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).
Yildirim, I., Belledonne, M., Freiwald, W. A. & Tenenbaum, J. B. Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).PDF icon eaax5979.full_.pdf (3.22 MB)
Tian, L., Ellis, K., Kryven, M. & Tenenbaum, J. B. Learning abstract structure for drawing by efficient motor program induction. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://papers.nips.cc/paper/2020/hash/1c104b9c0accfca52ef21728eaf01453-Abstract.html>
Nye, M., Solar-Lezama, A., Tenenbaum, J. B. & Lake, B. M. Learning Compositional Rules via Neural Program Synthesis. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/7a685d9edd95508471a9d3d6fcace432-Abstract.html>PDF icon 2003.05562.pdf (2.51 MB)
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
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)
Allen, K., Smith, K. A. & Tenenbaum, J. B. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences 201912341 (2020). doi:10.1073/pnas.1912341117PDF icon 1912341117.full_.pdf (2.15 MB)
Dasgupta, I., Schulz, E., Tenenbaum, J. B. & Gershman, S. J. A theory of learning to infer. Psychological Review 127, 412 - 441 (2020).
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/>
Eisape, T., Levy, R., Tenenbaum, J. B. & Zaslavsky, N. Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).
2019
Kryven, M., Niemi, L., Paul, L. & Tenenbaum, J. B. Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Kryven, M., Scholl, B. & Tenenbaum, J. B. Does intuitive inference of physical stability interruptattention?. Cognitive Sciences Society (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)
Serrino, J., Kleiman-Weiner, M., Parkes, D. C. & Tenenbaum, 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)

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