Categorization as nonparametric Bayesian density estimation

Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


The authors apply the state of the art techniques from machine learning and statistics to reconceptualize the problem of unsupervised category learning, and to relate it to previous psychologically motivated models, especially Anderson's rational analysis of categorization. The resulting analysis provides a deeper understanding of the motivations underlying the classic models of category representation, based on prototypes or exemplars, as well as shedding new light on the empirical data. Exemplar models assume that a category is represented by a set of stored exemplars, and categorizing new stimuli involves comparing these stimuli to the set of exemplars in each category. Prototype models assume that a category is associated with a single prototype and categorization involves comparing new stimuli to these prototypes. These approaches to category learning correspond to different strategies for density estimation used in statistics, being nonparametric and parametric density estimation respectively.

Original languageEnglish (US)
Title of host publicationThe Probabilistic Mind
Subtitle of host publicationProspects for Bayesian cognitive science
PublisherOxford University Press
ISBN (Electronic)9780191695971
ISBN (Print)9780199216093
StatePublished - Mar 22 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology


  • Categorization
  • Category learning
  • Density estimation
  • Exemplar models
  • Machine learning
  • Prototype models
  • Statistics


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