Locally Bayesian Learning With Applications to Retrospective Revaluation and Highlighting

• Published in: Psychological review Vol. 113; no. 4; pp. 677 - 699
• Main Author: Kruschke, John K
• Format: Journal Article
• Language: English
• Published: Washington, DC American Psychological Association 01-10-2006
• Subjects: Activity levels. Psychomotricity; Associative Learning; Attention; Bayes Theorem; Bayesian analysis; Bayesian method; Bayesian Statistics; Behavior; Biological and medical sciences; Choice Behavior; Cognition; Evaluation Methods; Fundamental and applied biological sciences. Psychology; Human; Human behaviour; Humans; Learning; Learning Theory; Learning. Memory; Mathematical Modeling; Probability; Psychology; Psychology - statistics & numerical data; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Retrospective Studies; Selective Attention; Statistical Probability; Statistics; Theory; Vigilance. Attention. Sleep; statistical probability; learning theory; selective attention; Learning; Human; Backpropagation; Selective attention; Acquisition process; Theory; Cognition; Simulation model; Mathematical model; Connectionism
• ISSN: 0033-295X; 1939-1471
• DOI: 10.1037/0033-295X.113.4.677

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component's target is the input to the next component that maximizes the probability of the next component's target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.