title: Within-category feature correlations and Bayesian adjustment strategies.
abstract: To the extent that categories inform judgments about items, the accuracy with which categories capture the statistical structure of experience should affect judgment accuracy. The authors argue that representations of feature correlations can serve as Bayesian priors, increasing the accuracy of stimulus estimates by decreasing variability. Participants viewed a series of objects that varied on two dimensions that were either uncorrelated or correlated. They estimated each item by manipulating a response object to make it match the presented stimulus. Subsequent classification and feature-inference tasks indicated that the correlation was detected. The pattern of variability in recollections of stimuli suggested that the feature correlation informed estimates as predicted by a Bayesian model of category effects on memory.
authors.affiliation: Department of Psychology, University of Richmond, Richmond, VA 23173, USA. lcrawfor@richmond.edu
authors.forename: L Elizabeth
authors.initials: LE
authors.lastname: Crawford
authors.forename: Janellen
authors.initials: J
authors.lastname: Huttenlocher
authors.forename: Larry V
authors.initials: LV
authors.lastname: Hedges
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007600:Judgment; D008568:Memory; D011584:Psychology / Q000706:statistics & numerical data*; D017603:Signal Detection, Psychological