title: Detecting intra- and inter-categorical structure in semantic concepts using HICLAS.
abstract: In this paper, we investigate the hypothesis that people use feature correlations to detect inter- and intra-categorical structure. More specifically, we study whether it is plausible that people strategically look for a particular type of feature co-occurrence that can be represented in terms of rectangular patterns of 1s and 0s in a binary feature by exemplar matrix. Analyzing data from the Animal and Artifact domains, we show that the HICLAS model, which looks for such rectangular structure and which therefore models a cognitive capacity of detecting feature co-occurence in large data bases of features characterizing exemplars, succeeds rather well in predicting inter- and intra-categorical structure.
authors.affiliation: Department of Educational Sciences, University of Leuven, Belgium. eva.ceulemans@ped.kuleuven.be
authors.forename: Eva
authors.initials: E
authors.lastname: Ceulemans
authors.forename: Gert
authors.initials: G
authors.lastname: Storms
mesh_terms: D003071:Cognition; D006801:Humans; D008960:Models, Psychological; D012660:Semantics; D017603:Signal Detection, Psychological