title: Sorting out categories: incremental learning of category structure.
abstract: Two experiments examine how inferences might promote unsupervised and incremental category learning. Many categories have members related through overall similarity (e.g., a family resemblance structure) rather than by a defining feature. However, when people are asked to sort category members in a category construction task, they often do so by partitioning on a single feature. Starting from an earlier result showing that pairwise inferences increase family resemblance sorting (Lassaline & Murphy, 1996), we examine how these inferences lead to learning the family resemblance structure. Results show that the category structure is learned incrementally. The pairwise inferences influence participants' weightings of feature pairs that were specifically asked about, which in turn affects their sorting. The sorting then allows further learning of the categorical structure. Thus, the inferences do not directly lead learners to the family resemblance structure, but they do provide a foundation to build on as the participants make additional judgments.
authors.affiliation: Department of Psychology, University of Illinois, 603 East Daniel Street, Champaign, IL 61820, USA. mikediaz@uiuc.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Diaz
authors.forename: Brian H
authors.initials: BH
authors.lastname: Ross
mesh_terms: D006801:Humans; D007600:Judgment; D007858:Learning; D011795:Surveys and Questionnaires