title: Modeling the roles of category and feature information in inference.
abstract: The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known.
authors.affiliation: University of Massachusetts, <location>Amherst, MA, USA</location>
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Zivot
authors.affiliation: University of Massachusetts, <location>Amherst, MA, USA</location>
authors.forename: Andrew L
authors.initials: AL
authors.lastname: Cohen
mesh_terms: D000161:Acoustic Stimulation; D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010465:Perception / Q000502:physiology*; D010775:Photic Stimulation; D011930:Reaction Time; D013647:Task Performance and Analysis
keywords: categorization; fuzzy logical model of perception; inference; information integration; linear integration model