title: The helpfulness of category labels in semi-supervised learning depends on category structure.
abstract: The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people's responses are driven by the specific set of labels they see. We present an extension of Anderson's Rational Model of Categorization that captures this effect.
authors.affiliation: School of Psychology, University of Adelaide, Adelaide, Australia. waikeen.vong@adelaide.edu.au.
authors.forename: Wai Keen
authors.initials: WK
authors.lastname: Vong
authors.affiliation: School of Psychology, University of Adelaide, Adelaide, Australia.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Navarro
authors.affiliation: School of Psychology, University of Adelaide, Adelaide, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Perfors
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002965:Classification; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D008962:Models, Theoretical; D055815:Young Adult
keywords: Category learning; Computational modeling; Semi-supervised learning