title: Uncovering contrast categories in categorization with a probabilistic threshold model.
abstract: A contrast category effect on categorization occurs when the decision to apply a category term to an entity not only involves a comparison between the entity and the target category but is also influenced by a comparison of the entity with 1 or more alternative categories from the same domain as the target. Establishing a contrast category effect on categorization in natural language categories has proven to be laborious, especially when the categories concerned are natural kinds situated at the superordinate level of abstraction. We conducted 3 studies with these categories to look for an influence on categorization of both similarity to the target category and similarity to a contrast category. The results are analyzed with a probabilistic threshold model that assumes categorization decisions arise from the placement of threshold criteria by individual categorizers along a single scale that holds the experimental stimuli. The stimuli's positions along the scale are shown to be influenced by similarity to both target and contrast. These findings suggest that the prevalence of contrast category effects on categorization might have been underestimated. Additional analyses demonstrate how the proposed model can be employed in future studies to systematically investigate the origins of contrast category effects on categorization.
authors.affiliation: Department of Psychology, University of Leuven, Tiensestraat 102, Box 3721, BE-3000 Leuven. steven.verheyen@psy.kuleuven.be
authors.forename: Steven
authors.initials: S
authors.lastname: Verheyen
authors.forename: Simon
authors.initials: S
authors.lastname: De Deyne
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Dry
authors.forename: Gert
authors.initials: G
authors.lastname: Storms
mesh_terms: D002965:Classification; D003210:Concept Formation; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D015233:Models, Statistical; D010775:Photic Stimulation; D012660:Semantics; D014796:Visual Perception