title: Bayes and blickets: effects of knowledge on causal induction in children and adults.
abstract: People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.
authors.affiliation: Department of Psychology, University of California, Berkeley, CA 94720-1650, USA. tom_griffiths@berkeley.edu
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.forename: David M
authors.initials: DM
authors.lastname: Sobel
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Alison
authors.initials: A
authors.lastname: Gopnik
mesh_terms: D000328:Adult; D001245:Association Learning; D001499:Bayes Theorem; D002675:Child, Preschool; D003210:Concept Formation; D005260:Female; D006801:Humans; D019359:Knowledge; D008297:Male; D008960:Models, Psychological; D011337:Probability Learning; D011340:Problem Solving