title: Error discounting in probabilistic category learning.
abstract: The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results were indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning.
authors.affiliation: School of Psychology, The University of Western Australia, Crawley, Western Australia, Australia.
authors.forename: Stewart
authors.initials: S
authors.lastname: Craig
authors.forename: Stephan
authors.initials: S
authors.lastname: Lewandowsky
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Little
mesh_terms: D000704:Analysis of Variance; D002755:Choice Behavior / Q000502:physiology; D003198:Computer Simulation; D003210:Concept Formation / Q000502:physiology*; D003463:Cues; D030141:Feedback, Psychological / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods; D011336:Probability; D011337:Probability Learning; D011601:Psychophysics; D011930:Reaction Time; D012691:Serial Learning / Q000502:physiology*