CoCoDev

Publications

2026

  1. Paul, L. A., Mills, T., Ullman, T. D., De Freitas, J., Colas, C., & Tenenbaum, J. B. (2026). Reverse engineering the centered self. Psychological Review, 133(4), 919.
  2. Hu, J., Sosa, F., & Ullman, T. (2026). People make graded judgments about the inconceivable. Cognition, 275, 106627.
  3. Balaban, H., & Ullman, T. D. (2026). Emotion may indirectly link rendering and social reasoning. Trends in Cognitive Sciences.
  4. Murthy, S., Zhao, R., Hu, J., Kakade, S., Wulfmeier, M., Qian, P., & Ullman, T. (2026). Cognitive models can reveal interpretable value trade-offs in language models. International Conference on Learning Representations (ICLR).
  5. Gizdov, A., Procopio, A., Li, Y., Harari, D., & Ullman, T. (2026). Human-Like Coarse Object Representations in Vision Models. arXiv:2602.12486.
  6. Shapira, N., Wendler, C., Yen, A., Sarti, G., Pal, K., Floody, O., Belfki, A., Loftus, A., Jannali, A. R., Prakash, N., et al. (2026). Agents of chaos. arXiv:2602.20021.
  7. Ullman, T. (2026). Non-negotiable problems for a negotiation framework of morality. Behavioral and Brain Sciences, 49, e157.
  8. Zhou, H., Balaban, H., & Ullman, T. D. (2026). How Mental Simulation Predicts Collisions. Proceedings of the Annual Meeting of the Cognitive Science Society, 48.
  9. Fu, S., Beger, C., Yi, R. C., Mitchell, M., Frank, M. C., Millière, R., Hawkins, R., & Ullman, T. D. (2026). Cognitive-Science–Inspired Evaluation of Large Language Models. Proceedings of the Annual Meeting of the Cognitive Science Society, 48.
  10. Chen, H., Chung, K., Bhandwaldar, A., Tenenbaum, J. B., Ullman, T. D., & Shu, T. (2026). POPKI: Modeling Surprise in a Physically-Grounded Joint Inference of Observation, Preference, and Knowledge. Open Mind, 10, 1157–1170.
  11. Brod, G., McAuliffe, K., & Ullman, T. (2026). Revisiting the 5-to 7-year shift: The age of metacognitive control? Child Development Perspectives.
  12. Zhang, A., Gershman, S. J., & Ullman, T. D. (2026). Reliability-guided meta-control in intuitive physics. bioRxiv preprint.
  13. Ying, L., Wu, J., Wang, Y. S., Aarya, S., Buschoff, L. M. S., Chen, H., Collins, K. M., de Varda, A., Fu, S., Houlihan, S. D., et al. (2026). CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition. arXiv:2609.21259.
  14. Qian, P., Li, A., Chen, S., Murthy, S. K., Belinkov, Y., & Ullman, T. D. (2026). Directing large language models to follow the letter or spirit of the law. arXiv:2609.23083.
  15. Wang, Y., Bigelow, E., Ullman, T., Tang, Y., & Risi, S. (2026). Integrating Simulation and Chain-of-thought Reasoning in Multimodal-Language Models For Physical Reasoning. arXiv:2610.19441.
  16. Chu, J., Rule, J. S., Goddu, M. K., Pinter, V., Reagan, E. R., Bonawitz, E., Gopnik, A., & Ullman, T. D. (2026). Fun isn't easy: Children selectively manipulate task difficulty when “playing for fun” versus “playing to win”. Developmental Psychology, 62(11), 2115–2127.

2025

  1. Sosa, F. A., Gershman, S. J., & Ullman, T. D. (2025). Blending simulation and abstraction for physical reasoning. Cognition, 254, 105995.
  2. Bridgers, S., Qian, P., Parece, K., Taliaferro, M., Schulz, L., & Ullman, T. D. (2025). Loopholes: A window into value alignment and the communication of meaning. Cognition, 261, 106131.
  3. Qian, P., & Ullman, T. D. (2025). Shape Guides Visual Pretense. Open Mind, 9, 2092–2113.
  4. Hu, J., Sosa, F., & Ullman, T. (2025). Shades of zero: Distinguishing impossibility from inconceivability. Journal of Memory and Language, 143, 104640.
  5. Bridgers, S., Parece, K., Iwasaki, I., Broski, A., Schulz, L., & Ullman, T. (2025). Learning loopholes: The development of intentional misunderstandings in children. Child Development, 96(3), 1066–1087.
  6. Murthy, S. K., Ullman, T., & Hu, J. (2025). One fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversity. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 11241–11258.
  7. Bigelow, E., Holtzman, A., Tanaka, H., & Ullman, T. (2025). Forking paths in neural text generation. International Conference on Learning Representations (ICLR).
  8. Hu, J., Sosa, F., & Ullman, T. (2025). Re-evaluating theory of mind evaluation in large language models. Philosophical Transactions B, 380(1932), 20230499.
  9. Bass, I., Colantonio, J., Aboody, R., Wong, M., Ullman, T., & Bonawitz, E. (2025). Children's sensitivity to automatic behavior relates to pedagogical reasoning and Theory of Mind. Frontiers in Developmental Psychology, 3, 1574528.
  10. Xiang, Y., Bigelow, E., Gerstenberg, T., Ullman, T. D., & Gershman, S. (2025). Language models assign responsibility based on actual rather than counterfactual contributions. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
  11. Balaban, H., & Ullman, T. D. (2025). Physics versus graphics as an organizing dichotomy in cognition. Trends in Cognitive Sciences, 29(11), 985–996.
  12. Murthy, S. K., Zhao, R., Hu, J., Kakade, S. M., Wulfmeier, M., Qian, P., & Ullman, T. (2025). Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs. First Workshop on CogInterp: Interpreting Cognition in Deep Learning Models.
  13. Balaban, H., & Ullman, T. D. (2025). The capacity limits of moving objects in the imagination. Nature Communications, 16(1), 5899.
  14. Okawa, M., Zhao, B., Bigelow, E. J., Yu, R., Ullman, T., Lubana, E. S., & Tanaka, H. (2025). Emergence of hierarchical emotion organization in large language models. arXiv:2507.10599.
  15. Bigelow, E. J., Ahmed, Z., & Ullman, T. (2025). Evaluating Self-Orienting in Language and Reasoning Models. ICML 2025 Workshop on Assessing World Models.
  16. Wang, Y., & Ullman, T. (2025). Resource bounds on mental simulations: Evidence from a liquid-reasoning task. Journal of Experimental Psychology: General.
  17. Chu, J., O'Keeffe, M., Liu, S. K., Bonawitz, E., & Ullman, T. D. (2025). Stumped! Learning to think outside the box in 3-7 year old children. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
  18. Parece, K., Ullman, T. D., & Schulz, L. (2025). Intuitions about prosocial backfiring: Four to seven-year olds' understanding of when helping might cause offense. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
  19. Hu, J., Sosa, F. A., & Ullman, T. D. (2025). Making Sense of Nonsense. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
  20. Bhattacharya, A. R., Hu, J., & Ullman, T. D. (2025). The Uncanny Valley meets the Humorous Hill: Things are funny when they match a pattern but fall short on quality. Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
  21. Bigelow, E., & Ullman, T. (2025). People evaluate agents based on the algorithms that drive their behavior. Open Mind, 9, 1411–1430.
  22. Bigelow, E., Wurgaft, D., Wang, Y., Goodman, N., Ullman, T., Tanaka, H., & Lubana, E. S. (2025). Belief dynamics reveal the dual nature of in-context learning and activation steering. arXiv:2511.00617.
  23. Wang, Y., Bigelow, E., Li, B., & Ullman, T. (2025). Chain of Time: In-Context Physical Simulation with Image Generation Models. arXiv:2511.00110.
  24. Gizdov, A., Procopio, A., Li, Y., Harari, D., & Ullman, T. (2025). Towards aligned body representations in vision models. arXiv:2512.00365.
  25. Li, Y., Hammond, V., & Ullman, T. (2025). Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction. PsyArXiv preprint.
  26. Bigelow, E., Xiang, Y., Gerstenberg, T., Ullman, T., & Gershman, S. J. (2025). Actual or counterfactual? Asymmetric responsibility attributions in language models. First Workshop on CogInterp: Interpreting Cognition in Deep Learning Models.

2024

  1. Bigelow, E., Holtzman, A., Tanaka, H., & Ullman, T. (2024). Forking Paths in Neural Text Generation. arXiv:2412.07961.
  2. Ullman, T. (2024). The Illusion-Illusion: Vision Language Models See Illusions Where There are None. arXiv:2412.18613.
  3. Ullman, T. D., & Bass, I. (2024). The detection of automatic behavior in other people. American Psychologist, 79(9), 1322.
  4. Conwell, C., Tawiah-Quashie, R., & Ullman, T. (2024). Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models. arXiv:2411.17066.
  5. Balaban, H., Smith, K. A., Tenenbaum, J. B., & Ullman, T. D. (2024). Electrophysiology reveals that intuitive physics guides visual tracking and working memory. Open Mind, 8, 1425–1446.
  6. Kryven, M., Yu, S., Kleiman-Weiner, M., Ullman, T., & Tenenbaum, J. (2024). Approximate planning in spatial search. PLOS Computational Biology, 20(11), e1012582.
  7. Murthy, S. K., Ullman, T., & Hu, J. (2024). One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity. arXiv:2411.04427.
  8. Qian, P., Bridgers, S., Taliaferro, M., Parece, K., & Ullman, T. D. (2024). Ambivalence by design: A computational account of loopholes. Cognition, 252, 105914.
  9. Balaban, H., & Ullman, T. (2024). One at a time in the mind's eye: Serial versus parallel mental simulation of moving objects. Journal of Vision, 24(10), 281.
  10. Qian, P., Parece, K., & Ullman, T. D. (2024). People's intuitive reasoning about deniability of indirect speech act.
  11. Bass, I., Espinoza, C., Bonawitz, E., & Ullman, T. D. (2024). Teaching Without Thinking: Negative Evaluations of Rote Pedagogy. Cognitive Science, 48, e13470.
  12. Balaban, H., & Ullman, T. (2024). The capacity limits of tracking in the imagination.
  13. Balaban, H., & Ullman, T. D. (2024). Serial vs. Parallel Mental Simulation – Control Experiment.
  14. Bass, I., Espinoza, C., Bonawitz, E., & Ullman, T. D. (2024). Explanations for repetitiveness in evaluations of automaticity.
  15. Wood, J. N., Ullman, T. D., Wood, B. W., Spelke, E. S., & Wood, S. M. (2024). Object permanence in newborn chicks is robust against opposing evidence. arXiv:2402.14641.
  16. Qian, P., & Ullman, T. (2024). Shape Guides Visual Pretense.
  17. Jin, C., Wu, Y., Cao, J., Xiang, J., Kuo, Y. L., Hu, Z., … & Shu, T. (2024). MMToM-QA: Multimodal theory of mind question answering. arXiv:2401.08743.
  18. Parece, K., Schulz, L., & Ullman, T. D. (2024). Prosocial Backfiring: Helping & Competence.
  19. Parece, K., Ullman, T. D., & Schulz, L. (2024). Prosocial Backfiring: Competence & Helping.
  20. Balaban, H., & Ullman, T. (2024). The capacity limits of mental simulation.
  21. Chu, J., Hu, J., & Ullman, T. (2024). The Task Task: Creative problem generation in humans and language models. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.
  22. Parece, K., Bridgers, S., Ullman, T., & Schulz, L. (2024). Exploring Loophole Behavior: A Comparative Study of Autistic and Non-Autistic Populations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.
  23. Hu, J., Sosa, F. A., & Ullman, T. (2024). Shades of Zero: Distinguishing impossibility from inconceivability. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.
  24. Ullman, T. D., & Bridgers, S. (2024). Genies, lawyers, and smart-asses: Extending proxy failures to intentional misunderstandings. Behavioral and Brain Sciences.
  25. Bonawitz, E., & Ullman, T. D. (2024). Bayesian models of cognitive development. In Bayesian Models of Cognition: Reverse Engineering the Mind.
  26. Smith, K. A., Hamrick, J. B., Sanborn, A. N., Battaglia, P. W., Gerstenberg, T., Ullman, T. D., & Tenenbaum, J. B. (2024). Intuitive physics as probabilistic inference. In Bayesian Models of Cognition: Reverse Engineering the Mind.
  27. Jara-Ettinger, J., Baker, C., Ullman, T. D., & Tenenbaum, J. B. (2024). Theory of mind and inverse decision-making. In Bayesian Models of Cognition: Reverse Engineering the Mind.
  28. Jonusaite, S., & Ullman, T. D. (2024). The Invisible Hand as an Intuitive Sociological Explanation. Journal of Experimental Social Psychology.

2023

  1. Bigelow, E. J., Lubana, E. S., Dick, R. P., Tanaka, H., & Ullman, T. D. (2023). In-Context Learning Dynamics with Random Binary Sequences. arXiv preprint.
  2. Parece, K., Bridgers, S. E. C., Qian, P., Schulz, L., & Ullman, T. (2023). Skirting the Sacred: Moral Contexts Increase the Cost of Intentional Misunderstandings. PsyArXiv preprint.
  3. 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, ICML.
  4. 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.
  5. 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 Behaviour.
  6. Qian, P., Bridgers, S. E. C., Taliaferro, M., Parece, K., & Ullman, T. (2023). Ambivalence by Design: A Computational Account of Loopholes. PsyArXiv preprint.
  7. 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.
  8. 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.
  9. Paul, L. A., Ullman, T., De Freitas, J., & Tenenbaum, J. (2023). Reverse-engineering the Self. PsyArXiv preprint.
  10. Wang, Y., & Ullman, T. D. (2023). Resource bounds on mental simulations: Evidence from a fluid-reasoning task. PsyArXiv preprint.
  11. 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.
  12. Balaban, H., Smith, K., Tenenbaum, J., & Ullman, T. D. (2023). Neural evidence that intuitive physics guides visual tracking and working memory. PsyArXiv preprint.
  13. Gershman, S., & Ullman, T. D. (2023). Causal Implicatures from Correlational Statements. PLOS ONE.
  14. Bigelow, E. J., McCoy*, J., & Ullman*, T. (2023). Non-Commitment in Mental Imagery. Cognition.
  15. 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., & Kiela, D. (2023). Rethink reporting of evaluation results in AI. Science.
  16. Boger, T., & Ullman, T. (2023). What is "where": Physical reasoning informs object location. Open Mind.
  17. Ullman, T. (2023). Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks. arXiv:2302.08399.
  18. De Freitas, J., Uğuralp, A. K., Uğuralp, Z., Kim, P., & Ullman, T. D. (2023). Summarizing the Mental Customer Journey. Harvard Business School Working Paper.

2022

  1. Shu, T., Magaro, A., Kryven, M., Ullman, T., & Tenenbaum, J. (2022). Social Attribution Guides Similarity Judgment of Abstract Scenes. Journal of Vision.
  2. 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.
  3. 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.
  4. Bass, I., Espinoza, C., Bonawitz, E., & Ullman, T. (2022). Negative Evaluation of Rote Learning. PsyArXiv.
  5. Sosa, F. A., & Ullman, T. (2022). Type theory in human-like learning and inference. arXiv:2210.01634.
  6. 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.
  7. Bass, I., Smith, K., Bonawitz, E., & Ullman, T. D. (2022). Partial Mental Simulation Explains Fallacies in Physical Reasoning. Cognitive Neuropsychology. paper · PsyArXiv
  8. Gjata, N. N., Ullman, T. D., Spelke, E. S., & 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.
  9. Conwell, C., & Ullman, T. D. (2022). Testing Relational Understanding in Text-Guided Image Generation. arXiv.

2021

  1. Bridgers, S., Schulz, L. E., & 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.
  2. Mao, J., Luo, Z., Gan, C., Tenenbaum, J. B., Wu, J., Kaelbling, L. P., & Ullman, T. D. (2021). Temporal and Object Quantification Networks. Thirtieth International Joint Conference on Artificial Intelligence (IJCAI).
  3. 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.
  4. Ullman, T. D. (2021). What are you talking about? Nature Human Behaviour.
  5. Shu, T., Bhandwaldar, A., Gan, C., Smith, K. A., Liu, S., Gutfreund, D., Spelke, E., Tenenbaum, J. B., & Ullman, T. D. (2021). AGENT: A Benchmark for Core Psychological Reasoning. Thirty-eighth International Conference on Machine Learning (ICML).
  6. Du, Y., Smith, K., Ullman, T. D., Tenenbaum, J. B., & Wu, J. (2021). Unsupervised Discovery of 3D Physical Objects From Video. International Conference on Learning Representations (ICLR).
  7. Kryven, M., Ullman, T. D., Cowan, W., & Tenenbaum, J. B. (2021). Plans or Outcomes: How do we attribute intelligence to others? Cognitive Science.

2020

  1. Ullman, T. D., & Tenenbaum, J. B. (2020). Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology. paper · free PsyArXiv version
  2. Zimmerman, S., & 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 · book
  3. Smith, K. A., Mei, L., Yao, S., Wu, J., Spelke, E., Tenenbaum, J. B., & 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.
  4. Ullman, T. D. (2020). Heroes of our own story: Self-image and rationalizing in thought experiments. Behavioral and Brain Sciences, 43. accepted manuscript · paper

2019

  1. McCoy, J., & 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.
  2. Smith, K.*, Mei, L.*, Yao, S., Wu, J., Spelke, E., Tenenbaum, J. B., & Ullman, T. D. (2019). Modeling expectation violation in intuitive physics with coarse probabilistic object representations. Advances in Neural Information Processing Systems. project page
  3. Bonawitz, E., Ullman, T. D., Gopnik, A., & 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.
  4. McCoy, J. P., & Ullman, T. D. (2019). Judgments of effort for magical violations of intuitive physics. PLOS ONE. OSF
  5. McCoy, J. P.*, Paul, L. A.*, & Ullman, T. D.* (2019). Modal Prospection. In Metaphysics and Cognitive Science, eds. Alvin Goldman and Brian McLaughlin. Oxford University Press.

2018 and Prior

  1. McCoy, J. P., & Ullman, T. D. (2018). A Minimal Turing Test. Journal of Experimental Social Psychology. OSF
  2. Gerstenberg, T., Ullman, T. D., Nagel, J., Kleiman-Weiner, M., Lagnado, D., & Tenenbaum, J. B. (2018). Lucky or clever? From changed expectations to attributions of responsibility. Cognition.
  3. Ullman, T. D., Spelke, E. S., Battaglia, P., & Tenenbaum, J. B. (2017). Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Sciences. accepted manuscript · paper
  4. Liu, S., Ullman, T. D., Tenenbaum, J. B., & Spelke, E. S. (2017). Ten-month-old infants infer the value of goals from the costs of actions. Science. preprint · OSF
  5. Ullman, T. D., Stuhlmüller, A., Goodman, N. D., & Tenenbaum, J. B. (2017). Learning physical parameters from dynamic scenes. Cognitive Psychology.
  6. Ullman, T. D., Alonso-Diaz, S., Ferrigno, S., Zahid, S., & 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.
  7. Chang, M. B., Ullman, T. D., Torralba, A., & Tenenbaum, J. B. (2017). A compositional object-based approach to learning physical dynamics. International Conference on Learning Representations (ICLR).
  8. Liu, S., Ullman, T. D., Tenenbaum, J. B., & 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.
  9. Kryven, M., Ullman, T. D., Cowan, W., & 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.
  10. Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences.
  11. Ullman, T. D., Xu, Y., & Goodman, N. D. (2016). The Pragmatics of Spatial Language. Proceedings of the 38th Annual Conference of the Cognitive Science Society.
  12. Kryven, M., Ullman, T. D., Cowan, W., & Tenenbaum, J. B. (2016). Outcome or Strategy? A Bayesian Model of Intelligence Attribution. Proceedings of the 38th Annual Conference of the Cognitive Science Society.
  13. Ullman, T. D., Siegel, M., Tenenbaum, J. B., & Gershman, S. J. (2016). Coalescing the vapors of human experience into a viable and meaningful comprehension. Proceedings of the 38th Annual Conference of the Cognitive Science Society.
  14. Gerstenberg, T., Ullman, T. D., Kleiman-Weiner, M., Lagnado, D. A., & Tenenbaum, J. B. (2014). Wins above replacement: Responsibility attributions as counterfactual replacements. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society.
  15. Ullman, T. D., Stuhlmüller, A., Goodman, N. D., & Tenenbaum, J. B. (2014). Learning physics from dynamical scenes. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society.
  16. Hamlin, J. K., Ullman, T. D., Tenenbaum, J. B., Goodman, N. D., & Baker, C. L. (2013). The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model. Developmental Science.
  17. Bonawitz, E. B., Ullman, T. D., Gopnik, A., & Tenenbaum, J. B. (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).
  18. Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory learning as stochastic search in the language of thought. Cognitive Development.
  19. Ullman, T. D.*, McCoy, J. M.*, Stuhlmüller, A., Gerstenberg, T., & Tenenbaum, J. B. (2012). Why blame Bob? Probabilistic generative models, counterfactual reasoning, and blame attribution. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society.
  20. Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review.
  21. Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2010). Theory acquisition as stochastic search. Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
  22. Ullman, T. D., Baker, C. L., Macindoe, O., Evans, O., Goodman, N. D., & Tenenbaum, J. B. (2010). Help or hinder: Bayesian models of social goal inference. Advances in Neural Information Processing Systems (Vol. 22, pp. 1874–1882).
  23. Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2009). Learning a theory of causality. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.