Bayes and blickets: Effects of knowledge on causal induction in children and adults

Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1407-1455
Number of pages49
JournalCognitive science
Issue number8
StatePublished - Nov 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Artificial Intelligence
  • Cognitive Neuroscience


  • Bayesian inference
  • Causal induction
  • Cognitive development
  • Knowledge effects


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