Bayesian Models of Inductive Learning

Bayesian Models of Inductive Learning Thomas L. Griffiths (tom griffiths@brown.edu)Department of Cognitive and Linguistic SciencesBrown University, Providence RI 02912 USACharles Kemp (ckemp@mit.edu)Joshua B. Tenenbaum (jbt@mit.edu)Department of Brain and Cognitive SciencesMassachusetts Institute of Technology, Cambridge MA 02139 USAMany of the central problems of cognitive science are background in Bayesian statistics and a level of mathe-problems of induction, calling for uncertain inferences matical sophistication appropriate for an audience withfrom limited data. How can people learn the meaning general interests in computational modeling.of a new word from just a few examples? What makes The tutorial will begin with a discussion of the howa set of examples more or less representative of a con- Bayesian models fit into the general project of develop-cept? What makes two objects seem more or less sim- ing formal models of cognition. We will then outlineilar? Why are some generalizations apparently based some of the basic principles of Bayesian statistics thaton all-or-none rules while others appear to be based on areofrelevancetomodelingcognition(Griffiths&Yuille,gradients of similarity? How do we infer the existence 2006), before turning to a series of case studies illus-of hidden causal properties or novel causal laws? This trating these methods, contrasting multiple models bothtutorial will introduce an approach to explaining these within the Bayesian ...