Computational Cognitive Science Lab

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CI Causal Induction
CD Cognitive Development
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning
E Education
F Foundations
IB Inductive Biases
NBM Nonparametric Bayesian Models
P Perception
PR Probabilistic Reasoning
RPM Rational Process Models
S&C Similarity and Categorization
SC Social Cognition
SML Statistical Models of Language

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Social Cognition
SC SML	Batista, R., & Griffiths, T. L. (2026). A Rational Analysis of the Effects of Sycophantic AI. (preprint)
RPM SC	Ham, H., Arumugam, D., Correa, C. G., Zhao, B., Griffiths, T. L., & Velez, N. (2026). Rational Teachers Should 'Lie' to Bounded Students. Proceedings of the 48th Annual Conference of the Cognitive Science Society. (pdf)
SC SML	Harootonian, S. K., Ho, M. K., Griffiths, T. L., Niv, Y., & Sucholutsky, I. (2026). Do Large Language Models Mentalize When They Teach?. (preprint)
SC SML	Hu, H., Marjieh, R., Collins, K. M., Li, C., Griffiths, T. L., Sucholutsky, I., & Jacoby, N. (2026). Why Human Guidance Matters in Collaborative Vibe Coding. (preprint)
DMRL SC	Mankewitz, J., Collins, K. M., Tenenbaum, J., Griffiths, T. L., & Hawkins, R. D. (2026). Balancing Competing Goals when Coordinating over Rule Construction. Proceedings of the Annual Meeting of the Cognitive Science Society, 48. (pdf)
SC	Oktar, K., Sumers, T., & Griffiths, T. L. (2025). Rational vigilance of intentions and incentives guides learning from advice. (preprint)
CEIL SC	Shiiku, S., Marjieh, R., Griffiths, T. L., Anglada-Tort, M., & Jacoby, N. (2026). Hybrid Human-AI Societies Balance Creativity and Diversity. (preprint)
DMRL SC SML	Wu, A. J., Liu, R., Bai, X., & Griffiths, T. L. (2025). Large Language Models Develop Novel Social Biases Through Adaptive Exploration. Proceedings of the Fourty-third International Conference on Machine Learning (ICML). (pdf)
SC SML	Wu, A. J., Liu, R., Li, S. S., Tsvetkov, Y., & Griffiths, T. L. (2026). Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest. Second Conference on Language Modeling. (preprint)
CEIL SC	Ham, H., Zhao, B., Griffiths, T. L., & Velez, N. (2025). Teaching Recombinable Motifs Through Simple Example. Cognitive Science, 49(8). (pdf)
SC SML	Liu, R., Geng, J., Peterson, J. C., Sucholutsky, I., & Griffiths, T. L. (2025). Large language models assume people are more rational than we really are. Proceedings of the 13th International Conference on Learning Representations (ICLR).(pdf)
CEIL SC	Mieczkowski, E., Mon-Williams, R., Bramley, N., Lucas, C. G., Velez, N., & Griffiths, T. L. (2025). Predicting multi-agent specialization via task parallelizability. (preprint)
DMRL SC	Mieczkowski, E., Turner, C., Velez, N., & Griffiths, T. L. (2025) People evaluate idle collaborators based on their impact on task efficiency. Cognition, 264, 106200. (pdf)
S&C SC	Sucholutsky, I., Collins, K. M., Malaviya, M., Jacoby, N., Liu, W., Sumers, T. R., Korakakis, M., Bhatt, U., Ho, M., Tenenbaum, J. B., Love, B., Pardos, Z. A., Weller, A., & Griffiths, T. L. (2025). Representational alignment supports effective teaching. ICLR 2025 Workshop on Bidirectional Human-AI Alignment (pdf)
PR SC SML	Wu, A. J., Liu, R., Oktar, K., Sumers, T. R., & Griffiths, T. L. (2025). Are Large Language Models Sensitive to the Motives Behind Communication? Advances in Neural Information Processing Systems 39. (pdf)
DMRL SC	Bai, X., Griffiths, T. L., & Fiske, S. T. (2024). Costly exploration produces stereotypes with dimensions of warmth and competence. Journal of Experimental Psychology: General. (pdf)
SC SML	Bai, X., Wang, A., Sucholutsky, I., & Griffiths, T. L. (2024). Measuring implicit bias in explicitly unbiased large language models. (preprint)
CEIL SC	Barretto, D., Marjieh, R., & Griffiths, T. L. (2024). Reaching consensus through theory of mind in social networks with locally distributed interactions. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F SC	Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi-Xuan, T., Ho, M., Mansinghka, V., Weller, A., Tenenbaum, J. B., & Griffiths, T. L. (2024). Building machines that learn and think with people. Nature Human Behaviour, 8(10), 1851-1863. (pdf)
SC SML	Guo, X., Huang, K., Liu, J., Fan, W., Vélez, N., Wu, Q., Wang, H., & Griffiths, T. L. (2024). Embodied LLM agents learn to cooperate in organized teams. (preprint)
DMRL SC	Kuperwajs, I., van Opheusden, B., Russek, E., & Griffiths, T. L. (2024). Learning from rewards and social information in naturalistic strategic behavior. (preprint)
SC SML	Liu, R., Sumers, T. R., Dasgupta, I., & Griffiths, T. L. (2024). How do large language models navigate conflicts between honesty and helpfulness? Proceedings of the 41st International Conference on Machine Learning (ICML). (pdf)
DMRL SC	F. and Griffiths, T. L.hp?author=A.">A., Bullopublications.php?author=R.">R., Gokhaleblications.php?author=">Marjieh, R., Gokhale, A., Bullo, F. and Griffiths, T. L. (2024). Task allocation in teams as a multi-armed bandit. In Proceedings of Collective Intelligence 2024. (pdf)
DMRL SC	Mieczkowski, E., Turner, C. R., Vélez, N., & Griffiths, T. (2024). Many hands don't always make light work: Explaining social loafing via multiprocessing efficiency. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SC	Oktar, K., Lombrozo, T., & Griffiths, T. L. (2024). Learning from aggregated opinion. Psychological Science, 35(9), 1010–1024. (pdf)
DMRL SC	Oktar, K., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2024). Dimensions of disagreement: Unpacking divergence and misalignment in cognitive science and artificial intelligence. Decision, 11(4), 511–522. (pdf)
PR SC	Oktar, K., Sumers, T., & Griffiths, T. L. (2024). A rational model of vigilance in motivated communication. 46th Annual Meeting of the Cognitive Science Society. (pdf)
P SC	Urano, Y., Marjieh, R., Griffiths, T. L., & Jacoby, N. (2024). The influence of social information and presentation interface on aesthetic evaluations. 46th Annual Meeting of the Cognitive Science Society. (pdf)
CEIL SC	Hawkins, R. D., Franke, M., Frank, M. C., Goldberg, A. E., Smith, K., Griffiths, T. L., & Goodman, N. D. (2023). From partners to populations: A hierarchical Bayesian account of coordination and convention. Psychological Review, 130(4), 977–1016. (pdf)
SC SML	Liu, R., Yen, H., Marjieh, R., Griffiths, T. L., & Krishna, R. (2023). Improving interpersonal communication by simulating audiences with language models. 45th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SC	Bai, X., Fiske, S. T., & Griffiths, T. L. (2022). Globally inaccurate stereotypes can result from locally adaptive exploration. Psychological Science, 33(5) 671–684. (pdf)
P SC	Murthy, S. K., Hawkins, R. D., & Griffiths, T. L. (2022). Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. Cognition, 225, 105152. (pdf)
CEIL SC	Thompson, B., van Opheusden, B., Sumers, T., & Griffiths, T. L. (2022). Complex cognitive algorithms preserved by selective social learning in experimental populations. Science, 376(6588), 95-98. (pdf)
PR SC	Gates, V., Callaway, F., Ho, M. K., Griffiths, T. (2021). A rational model of people's inferences about others' preferences based on response times. Cognition, 217, 104885. (pdf)
PR SC	Hawkins, R. D., Liu, I., Goldberg, A. E., Griffiths, T. L. (2021). Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries. Proceedings of the 43rd Annual Conference of the Cognitive Science Society. (pdf)
SC	Whalen, A., Griffiths, T. L., & Buchsbaum, D. (2018). Sensitivity to shared information in social learning. Cognitive Science, 42(1), 168-187. (pdf)
SC SML	Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Evaluating theory of mind in question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. (pdf)
PR SC	Liu, C., Hamrick, J. B., Fisac, J. F., Dragan, A. D, Hendrick, J. K., Sastry, S. S, & Griffiths, T. L. (2016). Goal inference improves objective and perceived performance in human-robot collaboration. Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. (pdf)