A probabilistic model of theory formation

Charles Kemp, Joshua B. Tenenbaum, Sourabh Niyogi, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a domain, and the laws or principles that relate these concepts. We apply our model to several real-world problems, including learning the structure of kinship systems and learning ontologies. We also compare its predictions to data collected in two behavioral experiments. Experiment 1 shows that our model helps to explain how simple theories are acquired and used for inductive inference. Experiment 2 suggests that our model provides a better account of theory discovery than a more traditional alternative that focuses on features rather than relations.

Original languageEnglish (US)
Pages (from-to)165-196
Number of pages32
JournalCognition
Volume114
Issue number2
DOIs
StatePublished - Feb 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Cognitive Neuroscience

Keywords

  • Bayesian modeling
  • Conceptual structure
  • Relational learning
  • Systems of concepts

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