1997/01/01 by Robert M. Nosofsky, Thomas J. Palmeri · 3 citations
Neuroscience · Psychology · Computer Science · Mathematics · #Neural and Behavioral Psychology Studies #Child and Animal Learning Development #Advanced Text Analysis Techniques #Categorization #Automaticity #Similarity (geometry) #Context (archaeology) #Artificial intelligence #Perception #Object (grammar) #Random walk #Computer science #Machine learning #Pattern recognition (psychology) #Cognition #Psychology #Mathematics #Statistics
paper · doi:10.1037/0033-295x.104.2.266
openalex publication_date 1997/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/01
The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R. M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.