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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Decision Making and Reinforcement Learning
DMRL SML	Arumugam, D., & Griffiths, T. L. (2025). Toward efficient exploration by large language model agents. (preprint)
DMRL IB	Correa, C. G., Sanborn, S., Ho, M. K., Callaway, F., Daw, N. D., & Griffiths, T. L. (2025). Exploring the hierarchical structure of human plans via program generation. Cognition, 255, 105990. (pdf)
DMRL F	Turner, C. R., Arumugam, D., Nelson, L., & Griffiths, T. L. (2025). Trade-offs between tasks induced by capacity constraints bound the scope of intelligence. 47th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SML	Zhu, J. Q., Xie, H., Arumugam, D., Wilson, R. C., & Griffiths, T. L. (2025). Using reinforcement learning to train large language models to explain human decisions. (preprint)
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)
DMRL RPM	Cornell, C. A., Norman, K. A., Griffiths, T. L., & Zhang, Q. (2024). Improving memory search through model-based cue selection. Psychological Science, 35 (1), 55-71. (pdf)
DMRL	Correa, C. G., Griffiths, T. L., & Daw, N. D. (2024). Program-based strategy induction for reinforcement learning. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL P	Dubey, R., Hardy, M., Griffiths, T., & Bhui, R. (2024). AI-generated visuals of car-free American cities help increase support for sustainable transport policies. Nature Sustainability, 7, 399–403. (pdf)
DMRL SC	Kuperwajs, I., van Opheusden, B., Russek, E., & Griffiths, T. L. (2024). Learning from rewards and social information in naturalistic strategic behavior. (preprint)
DMRL	Mancoridis, M., Sumers, T., & Griffiths, T. (2024). Publish or perish: Simulating the impact of publication policies on science. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SC	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	Mieczkowski, E., Turner, C. R., Vélez, N., & Griffiths, T. (2024). People evaluate idle collaborators based on their impact on task efficiency. (preprint)
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)
DMRL SML	Peng, A., Sucholutsky, I., Li, B. Z., Sumers, T. R., Griffiths, T. L., Andreas, J., & Shah, J. A. (2024). Learning with language-guided state abstractions. Proceedings of the 12th International Conference on Learning Representations (ICLR). (pdf)
DMRL SML	Peng, A., Bobu, A., Li, B. Z., Sumers, T. R., Sucholutsky, I., Kumar, N., & Griffiths, T. L. (2024). Preference-conditioned language-guided abstraction. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. (pdf)
DMRL	Reichman, D., Peterson, J. C., & Griffiths, T. L. (2024). Machine learning for modeling human decisions. Decision, 11(4), 619. (pdf)
DMRL	Russek, E. M., Callaway, F., & Griffiths, T. L. (2024). Inverting cognitive models with neural networks to infer preferences from fixations. Cognitive Science, 48(11), e70015. (pdf)
DMRL SML	De Sabbata, C. N., Sumers, T. R., & Griffiths, T. L. (2024). Rational metareasoning for large language models. (preprint)
DMRL RPM	Zhao, B., Velez, N., & Griffiths, T. L. (2024). A rational model of innovation by recombination. 46th Annual Meeting of the Cognitive Science Society. (pdf)
CD DMRL	Zhao, B., Vélez, N., & Griffiths, T. L. (2024). Comparing human behavior to an optimal policy for innovation. Proceedings of the AAAI Symposium Series, 3(1), 598-599. (pdf)
DMRL	Zhu, J. Q., Peterson, J. C., Enke, B., & Griffiths, T. L. (2024) Capturing the complexity of human strategic decision-making with machine learning. (preprint)
DMRL SML	Zhu, J. Q., Yan, H., & Griffiths, T. L. (2024). Language models trained to do arithmetic predict human risky and intertemporal choice. (preprint)
DMRL	Agrawal, M., Peterson, J. C., Cohen, J. D., & Griffiths, T. L. (2023). Stress, intertemporal choice, and mitigation behavior during the COVID-19 pandemic. Journal of Experimental Psychology: General, 152(9), 2695–2702. (pdf)
DMRL RPM	Callaway, F., Griffiths, T. L., & Karreskog, G. (2023). Rational heuristics for one-shot games. (preprint)
DMRL RPM	Callaway, F., Griffiths, T. L., Norman, K. A., & Zhang, Q. (2023). Optimal metacognitive control of memory recall. Psychological Review, 131(3), 781–811. (pdf)
DMRL RPM	Callaway, F., Hardy, M., & Griffiths, T. L. (2023). Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures. Psychological Review, 130(6), 1457–1491. (pdf)
DMRL NBM	Chang, M., Dayan, A. L., Meier, F., Griffiths, T. L., Levine, S., & Zhang, A. (2023). Neural Constraint Satisfaction: Hierarchical abstraction for combinatorial generalization in object rearrangement. Proceedings of the 11th International Conference on Learning Representations. (pdf)
DMRL RPM	Correa, C. G., Ho, M. K., Callaway, F., Daw, N. D., Griffiths, T. L. (2023). Humans decompose tasks by trading off utility and computational cost. PLOS Computational Biology, 19(6), e1011087. (pdf)
CEIL DMRL	Hardy, M. D., Thompson, B., Krafft, P. M., & Griffiths, T. L. (2023). Resampling reduces bias amplification in experimental social networks. Nature Human Behavior, 7, 2084-2098. (pdf)
DMRL	He, R., Correa, C. G., Griffiths, T. L., & Ho, M. K. (2023). Structurally guided task decomposition in spatial navigation tasks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38. (pdf)
DMRL	Ho, M. K., Cohen, J. D., & Griffiths, T. L. (2023). Rational simplification and rigidity in human planning. Psychological Science, 34(11), 1281-1292. (pdf)
DMRL RPM	Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., He, R., Krueger, P. M., & Lieder, F. (2023). A computational process-tracing method for measuring people’s planning strategies and how they change over time. Behavior Research Methods, 55, 2037–2079. (pdf)
DMRL	Kumar, S., Dasgupta, I., Daw, N. D., Cohen, J. D., Griffiths, T. L. (2023). Disentangling abstraction from statistical pattern matching in human and machine learning. PLoS Computational Biology 19(8). (pdf)
DMRL	Peterson, J., Mancoridis, M., & Griffiths, T. (2023). To each their own theory: Exploring the limits of individual differences in decisions under risk. 45th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL IB	Rane, S., Ho, M., Sucholutsky, I., & Griffiths, T. L. (2023). Concept alignment as a prerequisite for value alignment. AAAI 2024 Bridge on Collaborative AI and Modeling of Humans. (pdf)
DMRL RPM	Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., & Griffiths, T. L. (2023). The computational challenges of means selection problems: Network structure of Goal Systems predicts human performance. Cognitive Science, 47(8), e13330. (pdf)
DMRL	Shin, M., Kim, J., van Opheusden, B., & Griffiths, T. L. (2023). Superhuman artificial intelligence can improve human decision-making by increasing novelty. Proceedings of the National Academy of Sciences, 120(12), e2214840120. (pdf)
DMRL RPM	Sukhov, N., Dubey, R., Duke, A., & Griffiths, T. (2023). When to keep trying and when to let go: Benchmarking optimal quitting. (preprint)
DMRL SML	Sumers, T. R., Ho, M. K., Griffiths, T. L., & Hawkins, R. D. (2023). Reconciling truthfulness and relevance as epistemic and decision-theoretic utility. Psychological Review 131 (1), 194. (pdf)
DMRL P	Turner, C. R., Morgan, T., & Griffiths, T. (2023). The joint evolution of sensory systems and decision policy allows cognition. 45th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL	Xia, F., Zhu, J., & Griffiths, T. (2023). Comparing human predictions from expert advice to on-line optimization algorithms. 45th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SML	Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 37. (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)
DMRL RPM	Callaway, F., Jain, Y. R., van Opheusden, B., Das, P., Iwama, G., Gul, S., Krueger, P. M., Becker, F., Griffiths, T. L., & Lieder, F. (2022). Leveraging artificial intelligence to improve people’s planning strategies. Proceedings of the National Academy of Sciences, 119(12), e2117432119. (pdf)
DMRL RPM	Callaway, F., van Opheusden, B., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2022). Rational use of cognitive resources in human planning. Nature Human Behaviour, 6, 1–14. (pdf)
DMRL F	Dubey, R., Griffiths, T. L., & Dayan, P. (2022). The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Computational Biology, 18(8), e1010316. (pdf)
DMRL RPM	Ho, M. K., Abel, D., Correa, C. G., Littman, M. L., Cohen, J. D., & Griffiths, T. L. (2022). People construct simplified mental representations to plan. Nature, 606(7912), 129-136. (pdf)
DMRL F	Ho, M. K., & Griffiths, T. L. (2022). Cognitive science as a source of forward and inverse models of human decisions for robotics and control. Annual Review of Control, Robotics, and Autonomous Systems, 5, 33-53. (pdf)
DMRL RPM	Russek, E., Acosta-Kane, D., van Opheusden, B., Mattar, M. G., & Griffiths, T. (2022). Time spent thinking in online chess reflects the value of computation. (preprint)
DMRL E	Sumers, T. R., Hawkins, R. D., Ho, M. K., Griffiths, T. L., & Hadfield-Menell, D. (2022). How to talk so AI will learn: Instructions, descriptions, and autonomy. Advances in Neural Information Processing Systems 36. (pdf)
DMRL RPM	Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLOS Computational Biology, 17(3), e1008863. (pdf)
DMRL E	Dubey, R., Ho, M. K., Mehta, H., & Griffiths, T. L. (2021). Aha! Moments correspond to meta-cognitive prediction errors. (preprint)
DMRL IB	Kumar, S., Dasgupta, I., Cohen, J. D., Daw, N. D., & Griffiths, T. L. (2021). Meta-learning of structured task distributions in humans and machines. Proceedings of the 9th International Conference on Learning Representations (ICLR). (pdf)
DMRL RPM	Milli, S., Lieder, F., & Griffiths, T. L. (2021). A rational reinterpretation of dual-process theories. Cognition, 217, 104881. (pdf)
DMRL	Peterson, J. C., Bourgin, D., Agrawal, M., Reichman, D., & Griffiths, T. (2021). Using large-scale experiments and machine learning to discover theories of human decision-making. Science, 372(6547), 1209-1214. (pdf)
DMRL SML	Sumers, T. R., Hawkins, R. D., Ho, M. K., & Griffiths, T. L. (2021). Extending rational models of communication from beliefs to actions. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SML	Sumers, T. R., Ho, M. K., Hawkins, R. D., Narasimhan, K. R., & Griffiths, T. L. (2021). Learning rewards from linguistic feedback. Proceedings of the 35th AAAI Conference on Artificial Intelligence. (pdf)
DMRL	Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2020). Scaling up psychology via Scientific Regret Minimization. Proceedings of the National Academy of Sciences. (pdf)
DMRL	Alon, N., Cohen, J. D., Griffiths, T. L., Manurangsi, P., Reichman, D., Shinkar, I., Wagner, T., Yu, A. (2020). Multitasking capacity: Hardness results and improved constructions. SIAM Journal on Discrete Mathematics, 34(1), 885-903. (pdf)
DMRL RPM	Callaway, F., Hardy, M., & Griffiths, T. L. (2020). Optimal nudging. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Chang, M., Kaushik, S., Weinberg, S. M., Griffiths, T., & Levine, S. (2020). Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. Proceedings of the International Conference on Machine Learning. (pdf)
DMRL RPM	Correa, C. G., Ho, M. K., Callaway, F., & Griffiths, T. L. (2020). Resource-rational Task Decomposition to Minimize Planning Costs. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL E	Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455-476. (pdf)
DMRL	Dubey, R., Grant, E., Luo, M., Narasimhan, K. R., & Griffiths, T. L. (2020). Connecting context-specific adaptation in humans to meta-learning. (preprint)
DMRL F	Gates, V., Griffiths, T. L., & Dragan, A. D. (2020). How to be helpful to multiple people at once. Cognitive Science, 44(6), e12841. (pdf)
DMRL	Ho, M. K., Abel, D., Cohen, J. D., Littman, M. L., & Griffiths, T. L. (2020). The Efficiency of Human Cognition Reflects Planned Information Processing. Proceedings of the 34th AAAI Conference on Artificial Intelligence. (pdf)
DMRL	Mormann, M., Griffiths, T. L., Janiszewski, C., Russo, J. E., Aribarg, A., Ashby, N. J., Bagchi, R., Bhatia, S., Kovacheva, M. M., & Mrkva, K. J. (2020). Time to pay attention to attention: using attention-based process traces to better understand consumer decision-making. Marketing Letters, 31, 381-392. (pdf)
DMRL P	Sumers, T. R., Ho, M. K., & Griffiths, T. L. (2020). Show or tell? Demonstration is more robust to changes in shared perception than explanation. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)
DMRL PR	Hardy, M., & Griffiths, T. L. (2019). Demonstrating the impact of prior knowledge in risky choice. (preprint)
DMRL	Lieder, F., Chen, O. X., Krueger, P. M., & Griffiths, T. L. (2019). Cognitive prostheses for goal achievement. Nature Human Behaviour, 3(10), 1096-1106. (pdf)
DMRL F	Ho, M. K., Abel, D., Griffiths, T. L., & Littman, M. L. (2019). The value of abstraction. Current Opinion in Behavioral Sciences, 29, 111-116. (pdf)
DMRL	Carroll, M., Shah, R., Ho, M. K., Griffiths, T., Seshia, S., Abbeel, P., & Dragan, A. (2019). On the Utility of Learning about Humans for Human-AI Coordination. In H. Wallach, H. Larochelle, A. Beygelzimer, F. Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32, 5174–5185. (pdf)
DMRL RPM	Chang, M. B., Gupta, A., Levine, S., & Griffiths, T. L. (2019). Automatically composing representation transformations as a means for generalization. Proceedings of the 7th International Conference on Learning Representations (ICLR) 2019. (pdf)
DMRL PR	Ho, M. K., Korman, J., & Griffiths, T. L. (2019). The computational structure of unintentional meaning. Proceedings of the 41st Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Bourgin, D., Peterson, J. C., Reichman, D., Russell, S., & Griffiths, T. L. (2019). Cognitive model priors for predicting human decisions. Proceedings of the 36th International Conference on Machine Learning (ICML). (pdf)
DMRL RPM	Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2019). Using machine learning to guide cognitive modeling: a case study in moral reasoning. Proceedings of the 41st Annual Conference of the Cognitive Science Society . (pdf)
DMRL IB	Lieder, F., Callaway, F., Jain, Y. R., Krueger, P. M., Das, P., Gul, S., & Griffiths, T. L. (2019). A cognitive tutor for helping people overcome present bias. Proceedings of the Fourth Multidisciplinary Conference on Reinforcement Learning and Decision Making. (pdf)
DMRL RPM	Reichman, D., Lieder, F., Bourgin, D. D., Talmon, N., & Griffiths, T. L. (2018). The computational challenges of pursuing multiple goals: Network structure of goal systems predicts human performance. (preprint)
DMRL RPM	Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T.L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology, 14, e1006043. (pdf)
DMRL RPM	Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018). Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review, 25, 775-784. (pdf)
DMRL RPM	Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review, 25, 322-349. (pdf)
DMRL PR RPM	Lieder, F., Griffiths, T. L., & Hsu, M (2018). Over-representation of extreme events in decision making reflects rational use of cognitive resources. Psychological Review, 125(1), 1-32. (pdf)
DMRL RPM	Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L., & Lieder, F. (2018). Learning to select computations. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence. (pdf)
DMRL	Burns, K., Nematzadeh, A., Grant, E., Gopnik, A., & Griffiths, T. L. (2018). Exploiting attention to reveal shortcomings in memory models. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 378-380. (pdf)
DMRL	Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L., & Efros, A. A. (2018). Investigating human priors for playing video games. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). (pdf) (project website)
DMRL	Krueger, P. M., & Griffiths, T. L. (2018). Shaping model-free habits with model-based goals. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
DMRL	Sanborn, S., Bourgin, D. D., Chang, M., & Griffiths, T. L. (2018). Representational efficiency outweighs action efficiency in human program induction. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
DMRL RPM	Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., & Griffiths, T. L. (2018). A resource-rational analysis of human planning. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
DMRL PR RPM	Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762-794. (pdf)
DMRL PR	Fisac, J. F., Gates, M. A., Hamrick, J. B., Liu, C., Hadfield-Menell, D., Palaniappan, M., Malik, D., Sastry, S. S., Griffiths, T. L., & Dragan, A. D. (2017). Pragmatic-Pedagogic Value Alignment. International Symposium on Robotics Research. (pdf)
DMRL	Krueger, P. M., Lieder, F., & Griffiths, T. L. (2017). Enhancing metacognitive reinforcement learning using reward structures and feedback. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)
DMRL RPM	Lieder, F., Krueger, P. M., & Griffiths, T. L. (2017). An automatic method for discovering rational heuristics for risky choice. Proceedings of the 39th Annual Conference of the Cognitive Science Society. (pdf)