All Topics
Foundations
Perception
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.)
Probabilistic Reasoning | |
| Abbott, J. T., Hamrick, J. B., & Griffiths, T. L. (in press). Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf) |
| Whalen, A., Buchsbaum, D., & Griffiths, T. L. (in press). How do you know that? Sensitivity to statistical dependency in social learning. Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pdf) |
| Williams, J. J., & Griffiths, T. L. (in press). Why are people bad at detecting randomness? A statistical argument. Journal of Experimental Psychology: Learning, Memory & Cognition. (pdf) |
| Schlerf, J., Xu, J., Klemfuss, N., Griffiths, T. L., & Ivry, R. B. (in press). Individuals with cerebellar degeneration show similar adaptation deficits with large and small visuomotor errors. Journal of Neurophysiology. (pdf) |
| Lieder, F., Griffiths, T. L., & Goodman, N. D. (2013). Burn-in, bias, and the rationality of anchoring. Advances in Neural Information Processing Systems, 25. (pdf) |
| Little, D., Lewandowsky, S., & Griffiths, T. L. (2012). A Bayesian model of rule induction in Raven's progressive matrices. Proceedings of the 34th Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., & Tenenbaum, J.B. (2011). Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General, 140, 725-743. (pdf) |
| Abbott, J. T., Heller, K. A., Ghahramani, Z., & Griffiths, T. L. (2011). Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems, 24. (pdf) |
| Austerweil, J. L., & Griffiths, T. L. (2011). Seeking confirmation is rational for deterministic hypotheses. Cognitive Science, 35, 499-526. (pdf) |
| Rafferty, A. N., Brunskill, E.B., Griffiths, T. L., & Shafto, P. (2011). Faster teaching by POMDP planning. Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED2011). (pdf) |
| Waisman, A. S., Lucas, C. G., Griffiths, T. L., Jacobs, L. F. (2011). A Bayesian model of navigation in squirrels. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pdf) |
| Shi, L., Griffiths, T. L., Feldman, N. H, & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17 (4), 443-464. (pdf) |
| Hsu, A., Griffiths, T. L., & Schreiber, E. (2010). Subjective randomness and natural scene statistics. Psychonomic Bulletin & Review, 17, 624-629. (pdf) |
| Bonawitz, E. B., & Griffiths, T. L. (2010). Deconfounding hypothesis generation and evaluation in Bayesian models. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Denison, S., Bonawitz, E. B., Gopnik, A., & Griffiths, T. L. (2010). Preschoolers sample from probability distributions. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. (pdf) |
| Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60, 63-106. (doi) |
| Shi, L., & Griffiths, T. L. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. Advances in Neural Information Processing Systems 22. (pdf) |
| Lewandowsky, S., Griffiths, T. L., & Kalish, M. L. (2009). The wisdom of individuals: Exploring peoples knowledge about everyday events using iterated learning. Cognitive Science, 33, 969-998. (pdf) |
| Vul, E., Goodman, N. D., Griffiths, T. L., & Tenenbaum, J. B. (2009). One and done? Optimal decisions from very few samples. Proceedings of the 31st Annual Conference of the Cognitive Science Society. (pdf) |
| Lucas, C., Griffiths, T. L., Xu, F., & Fawcett, C. (2009). A rational model of preference learning and choice prediction by children. Advances in Neural Information Processing Systems 21. (pdf) |
| Austerweil, J., & Griffiths, T. L. (2008). A rational analysis of confirmation with deterministic hypotheses. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf) |
| Williams, J. J., & Griffiths, T. L. (2008). Why are people bad at detecting randomness? Because it is hard. Proceedings of the 30th Annual Conference of the Cognitive Science Society. (pdf) |
| Schreiber, E., & Griffiths, T. L. (2007) Subjective randomness and natural scene statistics. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. (pdf) (article in The Economist) |
| Griffiths, T. L., & Tenenbaum, J. B. (2006). Statistics and the Bayesian mind. Significance, 3, 130-133. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2004). From algorithmic to subjective randomness. Advances in Neural Information Processing Systems 16. (pdf) (winner of the Best Student Paper prize) |
| Griffiths, T. L., & Tenenbaum, J. B. (2003). Probability, algorithmic complexity, and subjective randomness. Proceedings of the 25th Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2001). Randomness and coincidences: Reconciling intuition and probability theory. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf) |
| Tenenbaum, J. B., & Griffiths, T. L. (2001). The rational basis of representativeness. Proceedings of the 23rd Annual Conference of the Cognitive Science Society. (pdf) |
| Griffiths, T. L., & Tenenbaum, J. B. (2000). Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. Proceedings of the 22nd Annual Conference of the Cognitive Science Society. (pdf) |