title: A further investigation of category learning by inference.
abstract: Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.
authors.affiliation: University of Illinois, Urbana, USA.
authors.forename: Amy L
authors.initials: AL
authors.lastname: Anderson
authors.forename: Brian H
authors.initials: BH
authors.lastname: Ross
authors.forename: Seth
authors.initials: S
authors.lastname: Chin-Parker
mesh_terms: D000328:Adult; D001245:Association Learning; D002965:Classification; D003210:Concept Formation; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D008297:Male