Cognitive Science Dialogue
University of California, Berkeley
Dr. Matt Jones,
University of Colorado, Boulder
More about Tom Griffiths
More about Matt Jones
Tuesday, February 21st, Swift 107
Bayesian Models of Cognition -- What do they really tell us?
Bayesian modeling is a powerful computational method for studying the human mind. In recent years, the Bayesian approach has made rapid gains across cognitive science. These models have been applied to a variety of cognitive problems, including causal reasoning, concept learning, and language acquisition.
However, the explosion of Bayesian models in cognitive science has also given rise to controversy. In a recent critique, Jones and Love (2011) argued that much of Bayesian modeling is uninsightful, because it largely ignores psychological processes and representations. Indeed, they compare “Bayesian Fundamentalist” approaches to Behaviorism, in that both ignore the mechanisms of cognition.
The dialogue will offer competing perspectives on the role Bayesian modeling should play in cognitive science. Dr. Matt Jones presents the challenge to Bayesian approaches, and Dr. Tom Griffiths, a leading practitioner of Bayesian modeling, will act as defender.
- Jones, M., & Love, B. C. (2011). Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34. 169-231. (responses included)
- Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling. Cambridge University Press.
- Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384.
- Perfors, A., Tenenbaum, J.B., Griffiths, T. L., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302-321.
- 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.