Publications

Bonawitz, E., and Ullman, T. D. (2024) Bayesian models of cognitive development. Bayesian Models of Cognition: Reverse Engineering the Mind

Smith, K. A., Hamrick, J. B., Sanborn, Adam N., Battaglia, P. W., Gerstenberg, T., Ullman, T. D. and Tenenbaum, J. B. (2024) Intuitive physics as probabilistic inference. Bayesian Models of Cognition: Reverse Engineering the Mind

Jara-Ettinger, J., Baker, C., Ullman, T. D. and Tenenbaum, J. B. (2024) Theory of mind and inverse decision-making. Bayesian Models of Cognition: Reverse Engineering the Mind

Jonusaite, S. and Ullman, T.D., (2024). The Invisible Hand as an Intuitive Sociological Explanation. Journal of Experimental Social Psychology.

Bigelow, E. J., Lubana, E. S., Dick, R. P., Tanaka, H., and Ullman, T. D. (2023). In-Context Learning Dynamics with Random Binary Sequences. arXiv preprint

Parece, K., Bridgers, S. E. C., Qian, P., Schulz, L., and Ullman, T. (2023). Skirting the Sacred: Moral Contexts Increase the Cost of Intentional Misunderstandings. psyarxiv preprint.

Murthy, S.K., Bridgers, S., Parece, K., Glassman, E., Ullman, T.D. (2023). Comparing the Evaluation and Production of Loophole Behavior in Children and Large Language Models. First Workshop on Theory of Mind in Communicating Agents at the Fortieth International Conference on Machine Learning.

Murthy, S.K., Parece, K., Bridgers, S., Qian, P., Ullman, T. (2023). Comparing the Evaluation and Production of Loophole Behavior in Humans and Large Language Models. EMNLP Findings.

De Freitas, J., Uğuralp, A. K., Oğuz-Uğuralp, Z., Paul, L. A., Tenenbaum, J., & Ullman, T. D. (2023). Self-orienting in human and machine learning. Nature Human Behavior.

Qian, P., Bridgers, S. E. C., Taliaferro, M., Parece, K., and Ullman, T. (2023). Ambivalence by Design: A Computational Account of Loopholes. psyarxiv preprint.

Bridgers, S. E. C., Taliaferro, M., Parece, K., Schulz, L., & Ullman, T. (2023). Loopholes: A window into value alignment and the communication of meaning. psyarxiv preprint.

Rule, J., Goddu, M., Chu, J., Pinter, V., Reagan, E. R., Bonawitz, E., & Ullman, T. (2023). Fun isn’t easy: Children choose more difficult options when playing for fun vs.trying to win. psyarxiv preprint.

Paul, L. A., Ullman, T., De Freitas, J., & Tenenbaum, J. (2023). Reverse-engineering the Self. psyarxiv preprint.

Wang, Y., and Ullman, T. D. (2023). Resource bounds on mental simulations: Evidence from a fluid-reasoning task psyarxiv preprint.

Li, Y., Wang, Y., Boger, T., Smith, K. A., Gershman, S. J., & Ullman, T. D. (2023). An approximate representation of objects underlies physical reasoning. JEP: General

Balaban, H., Smith, K., Tenenbaum, J., and Ullman, T.D. (2023). Neural evidence that intuitive physics guides visual tracking and working memory. psyarxiv preprint.

Gershman, S. and Ullman, T.D., (2023). Causal Implicatures from Correlational Statements. PLOS ONE.

Bigelow, E. J., McCoy,* J., & Ullman, T.* (2023). Non-Commitment in Mental Imagery. Cognition.

Burnell, R., Schellaert, W., Burden, J., Ullman, T.D., Martinez-Plumed, F., Tenenbaum, J.B., Rutar, D., Cheke, L.G., Sohl-Dickstein, J., Mitchell, M. and Kiela, D. (2023). Rethink reporting of evaluation results in AI. Science

Boger, T., & Ullman, T. (2023). What is “where”: Physical reasoning informs object location. Open Mind.

Ullman, T. (2023). Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks. arXiv preprint arXiv:2302.08399.

De Freitas, J., Uğuralp, A. K., Uğuralp, Z., Kim, P., & Ullman, T. D. (2023). Summarizing the Mental Customer Journey. Harvard Business School Working Paper.

Shu, T., Magaro, A., Kryven, M., Ullman, T., & Tenenbaum, J. (2022). Social Attribution Guides Similarity Judgment of Abstract Scenes. Journal of Vision.

Liu, S., Pepe, B., Ganesh Kumar, M., Ullman, T. D., Tenenbaum, J. B., & Spelke, E. S. (2022). Dangerous ground: One-year-old infants are sensitive to peril in other agents’ action plans. Open Mind.

Balaban, H., Smith, K. A., Ullman, T. D., & Tenenbaum, J. B. (2022). Using EEG to uncover the dynamics of physical expectation violation and resolution. Journal of Vision.

Bass, I., Espinoza, C., Bonawitz, E., & Ullman, T. (2022). Negative Evaluation of Rote Learning. psyarxiv.

Sosa, F. A., & Ullman, T. (2022). Type theory in human-like learning and inference. arXiv:2210.01634.

De Freitas, J., Uğuralp, A. K., Uğuralp, Z., Paul, L., Tenenbaum, J. B., & Ullman, T. D. (2022). What Would It Mean for a Machine to Have a Self?. Harvard Business School Marketing Unit Working Paper.

Bass, L., Smith, K, Bonawitz, E., and Ullman, T.D., (2022) Partial Mental Simulation Explains Fallacies in Physical Reasoning. Cognitive Neuropsychology. (paywall paper) (psyarxiv version)

Gjata, N. N., Ullman, T. D., Spelke, E. S., and Liu, S. (2022). What could go wrong: adults and children calibrate predictions and explanations of others' actions based on relative reward and danger. Cognitive Science.

Conwell, C., and Ullman, T.D., (2022). Testing Relational Understanding in Text-Guided Image Generation. arxiv.

Bridgers, S., Schulz, L.E., and Ullman, T.D. (2021). Loopholes, a Window into Value Alignment and the Learning of Meaning. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.

Mao, J., Luo, Z., Gan, C., Tenenbaum, J.B., Wu, J.,Kaelbling, J.P., and Ullman, T.D. (2021). Temporal and Object Quantification Networks. Thirtieth International Joint Conference on Artificial Intelligence (IJCAI).

Sosa, F. A., Ullman, T., Tenenbaum, J. B., Gershman, S. J., & Gerstenberg, T. (2021). Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology. Cognition.

Ullman, T. D. (2021). What are you talking about? Nature Human Behaviour.

Shu, T., Bhandwaldar, A., Gan, C., Smith, K.A., Liu, S., Gutfreund, D., Spelke, E., Tenenbaum, J.B. and Ullman, T.D. (2021). AGENT: A Benchmark for Core Psychological Reasoning. Thirty-eighth International Conference on Machine Learning (ICML).

Du, Y., Smith, K., Ullman, T.D., Tenenebaum, J.B., and Wu, J. (2021). Unsupervised Discovery of 3D Physical Objects From Video. International Conference on Learning Representations (ICLR).

Kryven, M., Ullman, T.D., Cowan, W., and Tenenbaum, J.B., (2021) Plans or Outcomes: How do we attribute intelligence to others? Cognitive Science.

Ullman, T. D., and Tenenbaum, J. B. (2020). Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology. (paywall paper) (free psyarxiv version)

Zimmerman, S, and Ullman, T.D. (2020) Models of Transformative Decision Making, in Transformative Experience: New Philosophical Essays, eds. Enoch Lambert and John Schwenkler, Oxford University Press. (accepted manuscript) (link to book).

Smith, K. A., Mei, L., Yao, S., Wu, J., Spelke, E., Tenenbaum, J. B., and Ullman, T. D. (2020). The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.

Ullman, T.D. (2020) Heroes of our own story: Self-image and rationalizing in thought experiments. Behavioral and Brain Sciences 43 (2020) (accepted manuscript) (link to paper).

McCoy, J. and Ullman, T. (2019) Transformative Decisions and Their Discontents. Part of a symposium on L.A. Paul's "Transformative Experience". Rivista Internazionale di Filosofia e Psicologia, 10(3), 339 - 345.

Smith, K.*, Mei, L.*, Yao, S., Wu, J., Spelke, E., Tenenbaum, J.B., and Ullman, T.D., (2019) Modeling expectation violation in intuitive physics with coarse probabilistic object representations. Advances in Neural Information Processing Systems (project page)

Bonawitz E., Ullman, T.D., Gopnik, A. and Tenenbaum, J.B. (2019), Sticking to the evidence? A Computational and behavioral case Study of micro-theory change in the domain of magnetism. Cognitive Science.

McCoy, J.P., and Ullman, T.D. (2019), Judgments of effort for magical violations of intuitive physics. PLOS ONE (osf)

McCoy, J.P.*, Paul, LA*, and Ullman, T.D.* (2019), Modal Prospection. Metaphysics and Cognitive Science, eds. Alvin Goldman and Brian McLaughlin. Oxford University Press (US)

McCoy, J.P., and Ullman, T.D. (2018), A Minimal Turing Test. Journal of Experimental Social Psychology (osf)

Gerstenberg, T., Ullman, T.D., Nagel, J., Kleiman-Weiner, M., Lagnado, D., and Tenenbaum, J.B. (2018), Lucky or clever? From changed expectations to attributions of responsibility. Cognition.

Ullman, T. D., Spelke, E.S., Battaglia, P. and Tenenbaum, J.B. (2017), Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Science (accepted manuscript) (link to paper)

Liu, S., Ullman, T. D., Tenenbaum, J.B., and Spelke, E.S., (2017), Ten-month-old infants infer the value of goals from the costs of actions. Science (preprint) (osf)

Ullman, T. D., Stuhlmüller, A., Goodman, N.D. and Tenenbaum, J.B. (2017), Learning physical parameters from dynamic scenes. Cognitive Psychology

Ullman, T. D., Alonso-Diaz, S., Ferringo, S., Zahid, S. and Kidd, C. (2017), Weight matters: The role of physical weight in non-physical language across age and culture. Proceedings of the 39th Annual Meeting of the Cognitive Science Society

Chang, M. B., Ullman, T. D., Torralba, A., and Tenenbaum, J. B. (2017), A compositional object-based approach to learning physical dynamics. International Conference on Learning Representations (ICLR)

Liu, S., Ullman, T. D., Tenenbaum, J. B., and Spelke, E. S. (2017). What’s worth the effort: Ten-month-old infants infer the value of goals from the costs of actions. Proceedings of the 39th Annual Meeting of the Cognitive Science Society

Kryven, M., Ullman, T. D., Cowan, W., and Tenenbaum, J. B. (2017). Thinking and guessing: Bayesian and empirical models of how humans search. Proceedings of the 39th Annual Meeting of the Cognitive Science Society

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2016), Building machines that learn and think like people. Behavioral and Brain Sciences

T. D. Ullman, Y. Xu and N. D. Goodman (2016), The Pragmatics of Spatial Language. Proceedings of the 38th Annual Conference of the Cognitive Science Society.

M. Kryven, T. D. Ullman, W. Cowan and J. B. Tenenbaum (2016), Outcome or Strategy? A Bayesian Model of Intelligence Attribution. Proceedings of the 38th Annual Conference of the Cognitive Science Society.

T. D. Ullman, M. Siegel, J. B. Tenenbaum and S. J. Gershman (2016), Coalescing the vapors of human experience into a viable and meaningful comprehension Proceedings of the 38th Annual Conference of the Cognitive Science Society.

T. Gerstenberg, T. D. Ullman, M. Kleiman-Weiner, D. A. Lagnado and J. B. Tenenbaum (2014), Wins above replacement: Responsibility attributions as counterfactual replacements. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science society.

T. D. Ullman, A. Stuhlmüller, N. D. Goodman, J. B. Tenenbaum (2014), Learning physics from dynamical scenes. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science society.

J. K. Hamlin, T. D. Ullman, J. B. Tenenbaum, N. D. Goodman and C. L. Baker (2013), The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model. Developmental Science.

E. B. Bonawitz, T. D. Ullman, A. Gopnik and J. B. Tenenbaum (2012), Sticking to the evidence? A computational and behavioral case study of micro-theory change in the domain of magnetism. ICDL (best paper award: experiment combined with computational model).

T. D. Ullman, N. D. Goodman and J. B. Tenenbaum (2012), Theory learning as stochastic search in the language of thought. Cognitive Development.

T. D. Ullman*, J. M. McCoy*, A. Stuhlmüller, T. Gerstenberg and J. B. Tenenbaum (2012), Why blame Bob? Probabilistic generative models, counterfactual reasoning, and blame attribution. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science society.

N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (2011), Learning a theory of causality. Psychological Review.

T. D. Ullman, N. D. Goodman and J. B. Tenenbaum (2010), Theory acquisition as stochastic search: Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.

T. D. Ullman, C. L. Baker, O. Macindoe, O. Evans, N. D. Goodman and J. B. Tenenbaum (2010), Help or hinder: Bayesian models of social goal inference. Advances in Neural Information Processing Systems (Vol. 22, pp. 1874-1882).

N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (2009), Learning a theory of causality. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.