Year: 2006
Type: article
Abstract: The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by clustering similar stimuli together using Bayesian inference.As computing the posterior distribution over all assignments of stimuli to clusters is intractable, an approximation algorithm is used.The original algorithm used in the RMC was an incremental procedure that had no guarantees for the quality of the resulting approximation.Drawing on connections between the RMC and models used in nonparametric Bayesian density estimation, we present two alternative approximation algorithms that are asymptotically correct.Using these algorithms allows the effects of the assumptions of the RMC and the particular inference algorithm to be explored separately.We look at how the choice of inference algorithm changes the predictions of the model.
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2765279935