Causal categorization with bayes nets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A theory of categorization is presented in which knowledge of causal relationships between category features is represented as a Bayesian network. Referred to as causal-wodel theory. this theory predicts that objects are classified as category members to the extent they are likely to have been produced by a categorysJ causal model. On this view, people have models of the world that lead them to expect a certain distribution of features in category members (e.g.. correlations between feature pairs that are directly connected by causal relationships), and consider exemplars good category members when they manifest those expectations. These expectations include sensitivity to higher-order feature interactions that emerge from the asymmetries inherent in causal relationships.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
PublisherNeural information processing systems foundation
ISBN (Print)0262042088, 9780262042086
StatePublished - 2002
Event15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada
Duration: Dec 3 2001Dec 8 2001

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other15th Annual Neural Information Processing Systems Conference, NIPS 2001
CountryCanada
CityVancouver, BC
Period12/3/0112/8/01

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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