Over the last several decades, there has been a wealth of illuminating work on processes implicated in social cognition. Much less has been done in articulating how we learn the contours of particular concepts deployed in social cognition, like the concept MENTALISTIC AGENT. Recent developments in learning theory afford new tools for approaching these questions. In this article, I describe some rudimentary ways in which learning theoretic considerations can illuminate philosophically important aspects of the MENTALISTIC AGENT concept. I maintain that (...) MENTALISTIC AGENT is an essentialized concept and that learning-theoretic considerations help explain why the concept is not tied to particular traits. (shrink)

The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, (...) only a few experiments on category construction or free sorting have been run and they suggest that people do not sort examples by the FR principle. We report several new experiments and a two‐stage model for category construction. This model is contrasted with a variety of other models with respect to their ability to account for when FR sorting will and will not occur. The experiments serve to identify one basis for FR sorting and to support the two‐stage model. The distinctive property of the two‐stage model is that it assumes that people impose more structure than the examples support in the first stage and that the second stage adjusts for this difference between preferred and perceived structure. We speculate that people do not simply assimilate probabilistic structures but rather organize them in terms of discrete structures plus noise. (shrink)