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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Foundations
F	Ku, A., Campbell, D., Bai, X., Geng, J., Liu, R., Marjieh, R., McCoy, R. T., Nam, A., Sucholutsky, I., Veselovsky, V., Zhang, L., Zhu, J. Q., & Griffiths, T. L. (2025). Using the tools of cognitive science to understand large language models at different levels of analysis. (preprint)
F IB	Ku, A., Griffiths, T. L., & Chan, S. (2025). Predictability shapes adaptation: An evolutionary perspective on modes of learning in transformers. (preprint)
F	Musslick, S., Bartlett, L. K., Chandramouli, S. H., Dubova, M., Gobet, F., Griffiths, T. L., Hullman, J., King, R. D., Kutz, J. N., Lucas, C. G., Mahesh, S., Pestilli, F., Sloman, S. J., & Holmes, W. R. (2025). Automating the practice of science: Opportunities, challenges, and implications. Proceedings of the National Academy of Sciences, 122(5), e2401238121. (pdf)
F	Snell, J. C., & Griffiths, T. L. (2025). Conformal prediction as Bayesian quadrature. (preprint)
F	Sucholutsky, I., Zhao, B., Hwang, H., Chen, A., Russakovsky, O., Griffiths, T. L. (2025). Learning a doubly-exponential number of concepts from few examples. (preprint)
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)
F	Ying, L., Collins, K. M., Wong, L., Sucholutsky, I., Liu, R., Weller, A., Shu, T., Griffiths, T. L., & Tenenbaum, J. B. (2025). On benchmarking human-like intelligence in machines. (preprint)
CEIL F	Allen, K., Brändle, F., Botvinick, M., Fan, J. E., Gershman, S. J., Gopnik, A., Griffiths, T. L., Hartshorne, J. K., Hauser, T. U., Ho, M., de Leeuw, J. R., Ma, W. J., Murayama, K., Nelson, J. D., van Opheusden, B., Pouncy, T., Rafner, J., Rahwan, I., Rutledge, R. B., Sherson, J., Şimşek, Ö., Spiers, H., Summerfield, C., Thalmann, M., Vélez, N., Watrous, A. J., Tenenbaum, J. B., & Schulz, E. (2024). Using games to understand the mind. Nature Human Behaviour, 8, 1035–1043. (pdf)
F SML	Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., Griffiths, T. L., Haridi, S., Jagadish, A. K., Ji-An, L., Kipnis, A., Kumar, S., Ludwig, T., Mathony, M., Mattar, M., Modirshanechi, A., Nath, S. S., Peterson, J. C., Rmus, M., Russek, E. M., Saanum, T., Scharfenberg, N., Schubert, J. A., Schulze Buschoff, L. M., Singhi, N., Sui, X., Thalmann, M., Theis, F., Truong, V., Udandarao, V., Voudouris, K., Wilson, R., Witte, K., Wu, S., Wulff, D., Xiong, H., & Schulz, E. (2024). Centaur: A foundation model of human cognition. (preprint)
F P	Campbell, D., Rane, S., Giallanza, T., De Sabbata, N., Ghods, K., Joshi, A., Ku, A., Frankland, S. M., Griffiths, T. L., & Cohen, J. D., & Webb, T. W. (2024). Understanding the limits of vision language models through the lens of the binding problem. Advances in Neural Information Processing Systems 38. (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)
F PR	Griffiths, T. L., Zhu, J. Q., Grant, E., & McCoy, R. T. (2024). Bayes in the age of intelligent machines. Current Directions in Psychological Science, 33(5), 283-291. (pdf)
F SML	Ichien, N., Bhatia, S., Ivanova, A., Webb, T., Griffiths, T., & Binz, M. (2024). Higher cognition in large language models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
CI F	Lu, Q., Nguyen, T. T., Zhang, Q., Hasson, U., Griffiths, T. L., & Zacks, J. M. (2024). Reconciling shared versus context-specific information in a neural network model of latent causes. Scientific Reports, 14(1), 16782. (pdf)
F PR	Malaviya, M., Sucholutsky, I., & Griffiths, T. L. (2024). Pushing the limits of learning from limited data. Proceedings of the AAAI Symposium Series, 3 (1), 559-561. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). Embers of autoregression show how large language models are shaped by the problem they are trained to solve. Proceedings of the National Academy of Sciences, 121(41), e2322420121. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. (preprint)
F P	Zuo, Y., Kayan, K., Wang, M., Jeon, K., Deng, J., & Griffiths, T. L. (2024). Towards foundation models for 3D vision: How close are we? International Conference on 3D Vision 2025. (preprint)
F IB	Griffiths, T. L., Kumar, S., & McCoy, R. T. (2023). On the hazards of relating representations and inductive biases. Behavioral and Brain Sciences, 46, e275. (pdf)
F	Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Achterberg, J., Tenenbaum, J. B., Collins, K. M., Hermann, K. L., Oktar, K., Greff, K., Hebart, M. N., Jacoby, N., Marjieh, R., Geirhos, R., Chen, S., Kornblith, S., Rane, S., Konkle, T., O'Connell, T. P., Unterthiner, T., Lampinen, A. K., Müller, K.-R., Toneva, M., & Griffiths, T. L. (2023). Getting aligned on representational alignment. (preprint)
F SML	Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023). Cognitive architectures for language agents. Transactions on Machine Learning Research 2024(preprint)
F	Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J., & Watts, D. J. (2022). Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences. Behavioral and Brain Sciences, 47, e33. (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 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 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)
F RPM	Griffiths, T. L. (2020). Understanding human intelligence via human limitations. Trends in Cognitive Sciences, 24(11), 873-883. (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)
F RPM	Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019). Doing more with less: meta-reasoning and meta-learning in humans and machines. Current Opinion in Behavioral Sciences, 29, 24-30. (pdf)
CEIL F	Krafft, P. M., & Griffiths, T. L. (2018). Levels of analysis in computational social science. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pdf)
F	Alon, N., Reichman, D., Shinkar, I., Wagner, T., Musslick, S., Cohen, J. D., Griffiths, T. L., Dey, B., & Ozcimder, K. (2017). A graph-theoretic approach to multitasking. Advances in Neural Information Processing Systems, 2100-2109. (pdf)
F	Paxton, A., & Griffiths, T. L.(2017). Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behavior Research Methods, 49(5), 1630-1638.(pdf)
CEIL F	Suchow, J. W., Bourgin, D. D., & Griffiths, T. L. (2017). Evolution in mind: Evolutionary dynamics, cognitive processes, and Bayesian inference. Trends in Cognitive Sciences, 21(7), 522-530. (pdf)
F P	Griffiths, T. L., Abbott, J. T., & Hsu, A. S. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588. (pdf)
CEIL F	Suchow, J. W., & Griffiths, T. L. (2016). Rethinking experiment design as algorithm design. CrowdML – NIPS '16 Workshop on Crowdsourcing and Machine Learning. (pdf)
F	Sanborn, A. N., & Griffiths, T. L. (2015). Exploring the structure of mental representations by implementing computer algorithms with people. In Raaijmakers, J. G. W., Criss, A. H., Goldstone, R. L., Nosofsky, R. M., & Steyvers, M. (Eds.). Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin. New York: Psychology Press. (pdf)
F RPM	Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7, 217-229. (pdf)
F	Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. B. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26, 539-541. (pdf)
F	Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23. (pdf)
F	Griffiths, T. L., Tenenbaum, J. B., & Kemp, C. (2012). Bayesian inference. In K. J. Holyoak & R. G. Morrison, (Eds.) Oxford Handbook of Thinking and Reasoning. Oxford: Oxford University Press. (book)
F	Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are). Psychological Bulletin, 138, 415-422. (pdf)
F RPM	Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4), 263-268. (pdf)
F	Griffiths, T. L., Austerweil, J. L., & Berthiaume, V. G. (2012). Comparing the inductive biases of simple neural networks and Bayesian models. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf)
CD F	Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302-321. (pdf)
CD F	Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. (pdf)
F	Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. (pdf)
F	Griffiths, T. L. (2010). Bayesian models as tools for exploring inductive biases. In M. Banich & D. Caccamise (Eds.) Generalization of knowledge: Multidisciplinary perspectives. New York: Psychology Press.
F	Griffiths, T. L. (2009). Connecting human and machine learning via probabilistic models of cognition. InterSpeech 2009. (pdf)
F	Griffiths, T. L., & Yuille, A. (2008). A primer on probabilistic inference. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press. (pdf)
F	Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling. Cambridge University Press. (pdf)
F	Griffiths, T. L., & Yuille, A. (2006). A primer on probabilistic inference. Trends in Cognitive Sciences. Supplement to special issue on Probabilistic Models of Cognition (volume 10, issue 7). (pdf)
CI F	Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309-318. (pdf)
F PR	Griffiths, T. L., & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3, 130-133. (pdf)