Unifying rational models of categorization via the hierarchical Dirichlet Process

Models of categorization make different representational as-sumptions, with categories being represented by prototypes, sets of exemplars, and everything in between. Rational mod-els of categorization justify these representational assumptions in terms of different schemes for estimating probability distri-butions. However, they do not answer the question of which scheme should be used in representing a given category. We show that existing rational models of categorization are spe-cial cases of a statistical model called the hierarchical Dirichlet process, which can be used to automatically infer a represen-tation of the appropriate complexity for a given category