Theory-based Bayesian models of inductive learning and reasoning

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

Research output: Contribution to journalArticlepeer-review

405 Scopus citations

Abstract

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Original languageEnglish (US)
Pages (from-to)309-318
Number of pages10
JournalTrends in Cognitive Sciences
Volume10
Issue number7
DOIs
StatePublished - Jul 1 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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