Typicality effects in contingency-shaped generalized equivalence classes.

Mark Galizio, Katherine L Stewart, Carol Pilgrim
Author Information
  1. Mark Galizio: Department of Psychology, University of North Carolina at Wilmington, 28403, USA. galizio@uncw.edu

Abstract

Two experiments were conducted using match-to-sample methodologies in an effort to model lexical classes, which include both arbitrary and perceptual relations between class members. Training in both experiments used a one-to-many mapping procedure with nonsense syllables as samples and eight sets of abstract stimuli as comparisons. These abstract stimuli differed along a number of dimensions, four of which were critical to the experimenter-defined class membership. Stimuli in some comparison sets included only one of the class-defining features, but stimuli in other sets included two, three, or all four of the critical features. After mastery of the baseline training, three types of probe tests were conducted: symmetry, transitivity/equivalence, and novel probe tests in which the training nonsense syllables served as samples, and comparisons were novel abstract stimuli that included one or more of the class-defining features. Symmetry and transitivity/equivalence probe tests showed that the stimuli used in training became members of equivalence classes. The novel stimuli also became class members on the basis of inclusion of any of the critical features. Thus these probe tests revealed the formation of open-ended generalized equivalence classes. In addition, typicality effects were observed such that comparison sets with more critical features were learned with fewer errors, responded to more rapidly, and judged to be better exemplars of the class. Contingency-shaped stimulus classes established through a match-to-sample procedure thus show several important behavioral similarities to natural lexical categories.

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Grants

  1. HD34265/NICHD NIH HHS

MeSH Term

Adult
Cognition
Concept Formation
Form Perception
Generalization, Psychological
Humans
Learning
Reaction Time
Visual Perception

Word Cloud

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