Unsupervised category learning with integral-dimension stimuli

TitleUnsupervised category learning with integral-dimension stimuli
Publication TypeJournal Article
Year of Publication2012
AuthorsEll, S. W., F Ashby G., & Hutchinson S.
JournalQuarterly Journal of Experimental Psychology
Volume65
Issue8
Pagination1537-1562
Date Published2012
ISSN1747-0226
KeywordsAdult, Attention, Concept Formation, Discrimination Learning, Feedback, Psychological, Female, Humans, Learning, Male, Memory, Pattern Recognition, Visual, Students, Task Performance and Analysis
Abstract

Despite the recent surge in research on unsupervised category learning, the majority of studies have focused on unconstrained tasks in which no instructions are provided about the underlying category structure. Relatively little research has focused on constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. The few studies that have addressed this issue have focused almost exclusively on stimuli for which it is relatively easy to attend selectively to the component dimensions (i.e., separable dimensions). In the present study, we investigated the ability of participants to learn categories constructed from stimuli for which it is difficult, if not impossible, to attend selectively to the component dimensions (i.e., integral dimensions). The experiments demonstrate that individuals are capable of learning categories constructed from the integral dimensions of brightness and saturation, but this ability is generally limited to category structures requiring selective attention to brightness. As might be expected with integral dimensions, participants were often able to integrate brightness and saturation information in the absence of feedback--an ability not observed in previous studies with separable dimensions. Even so, there was a bias to weight brightness more heavily than saturation in the categorization process, suggesting a weak form of selective attention to brightness. These data present an important challenge for the development of models of unsupervised category learning.

DOI10.1080/17470218.2012.658821
Alternate JournalQ J Exp Psychol (Hove)
PubMed ID22506861
Grant ListMH3760 / MH / NIMH NIH HHS / United States