Griffiths, T. (2026). Perceptrons, Learning from Mistakes, and a Step Towards AI: An Excerpt from The Laws of Thought: The Quest for a Mathematical Theory of the Mind. Notices of the American Mathematical Society, 73(5), 395-400. (pdf)
Gupta, M., Campbell, D. I., & Griffiths, T. L. (2026). Meta-Learning Captures Human-Like Geometric Sensitivity. Proceedings of the Annual Meeting of the Cognitive Science Society, 48(1), 1-10. (pdf)
Ku, A., Griffiths, T. L., & Chan, S. C. Y. (2026). An evolutionary perspective on modes of learning in Transformers. Proceedings of the 14th International Conference on Learning Representations (ICLR). (pdf)
Nam, A. J., Campbell, D. I., Griffiths, T. L., Cohen, J. D., & Leslie, S. J. (2026). Understanding Task Representations in Neural Networks via Bayesian Ablation. Proceedings of the Fifth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 323:196-221. (pdf)
Zhu, J. Q., & Griffiths, T. L. (2026). Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayeian inference. Psychological Review, 133(3), 619-635. (pdf)
Arumugam, D., & Griffiths, T. L. (2025). On Temporal Credit Assignment and Data-Efficient Reinforcement Learning. Finding the Frame Workshop at RLC(pdf)
Snell, J. C., & Griffiths, T. L. (2025). Conformal prediction as Bayesian quadrature. Proceedings of the 42nd International Conference on Machine Learning (ICML).(pdf)
McCoy, R. T., & Griffits, T. L. (2024) Meta-learning as a bridge between neural networks and symbolic Bayesian models. Behavioral and Brain Sciences. (response to "Meta-learned models of cognition"). (pdf)
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)
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)
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)
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)
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)
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)
Suchow, J. W., & Griffiths, T. L. (2016). Rethinking experiment design as algorithm design. CrowdML – NIPS '16 Workshop on Crowdsourcing and Machine Learning. (pdf)
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)
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)
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)
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)
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)
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.
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)
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)
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)