title: Transfer in Rule-Based Category Learning Depends on the Training Task.
abstract: While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning. Participants were presented with stimuli corresponding to four different categories and were asked to perform either a category discrimination task (which permits learning specific rule to discriminate two categories) or a category identification task (which does not permit learning a specific discrimination rule). In a subsequent transfer stage, all participants were asked to discriminate stimuli belonging to two of the categories which they had seen, but had never directly discriminated before (i.e., this particular discrimination was omitted from training). As predicted, learning in the category-discrimination tasks tended to be specific, while the category-identification task produced learning that transferred to the transfer discrimination task. These results suggest that the discrimination and identification tasks fostered the acquisition of different category representations which were more or less generalizable.
authors.affiliation: Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States of America.|Department of Psychology, Technische Universität Darmstadt, Darmstadt, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Kattner
authors.affiliation: Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States of America.
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Cox
authors.affiliation: Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States of America.
authors.forename: C Shawn
authors.initials: CS
authors.lastname: Green
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D055815:Young Adult