title: Inference and classification learning of abstract coherent categories.
abstract: Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction.
authors.affiliation: Department of Psychology, Yale University, New Haven, CT 06520-8205, USA. jane.erickson@yale.edu
authors.forename: Jane E
authors.initials: JE
authors.lastname: Erickson
authors.forename: Seth
authors.initials: S
authors.lastname: Chin-Parker
authors.forename: Brian H
authors.initials: BH
authors.lastname: Ross
mesh_terms: D003071:Cognition; D006801:Humans; D007858:Learning; D012660:Semantics