All Topics
Foundations
Perception
Education
Causal Induction
Cognitive Development
Probabilistic Reasoning
Rational Process Models
Similarity and Categorization
Statistical Models of Language
Nonparametric Bayesian Models
Cultural Evolution and Iterated Learning
(Click on an author's name to view all papers by that author.)
Foundations | |
| 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) |
| 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) |
| Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26, 539-541. (pdf) |
| Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23. (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., 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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. (2009). Connecting human and machine learning via probabilistic models of cognition. InterSpeech 2009. (pdf) |
| 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) |
| 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) |
| Griffiths, T. L., & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3, 130-133. (pdf) |