Categories are central to cognition, reflecting our knowledge of the structure of the world, supporting inferences, and serving as the basic units of thought. The process used by people to group objects into categories has been extensively studied, but generally using training paradigms with artificial categories. Drawing on a correspondence between human choice behavior and a popular statistical algorithm, a Markov chain Monte Carlo (MCMC) method is used to explore the subjective probability distributions of natural categories. This method does not make any distributional assumptions and allows arbitrary category structures to be determined. In addition, the MCMC method is combined with multidimensional scaling in order to describe natural category structures in a psychologically-relevant similarity space. We apply this method to determine the subjective probability distributions of basic-level categories of fruits and vegetables. These empirical distributions are compared to the characteristic subjective probability distributions of standard models of categorization. |