title: Theory-based Bayesian models of inductive learning and reasoning.
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.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. jtb@mit.edu
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.forename: Charles
authors.initials: C
authors.lastname: Kemp
mesh_terms: D001245:Association Learning / Q000502:physiology; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D032882:Comprehension / Q000502:physiology; D003210:Concept Formation / Q000502:physiology; D005793:Generalization, Psychological / Q000502:physiology*; D006801:Humans; D019545:Intuition / Q000502:physiology; D007858:Learning / Q000502:physiology*; D015233:Models, Statistical; D011338:Probability Theory; D011340:Problem Solving / Q000502:physiology*; D014706:Verbal Learning / Q000502:physiology