Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults

• Main Authors: Griffiths, Thomas L. (Author), Sobel, David M. (Author), Tenenbaum, Joshua B. (Contributor), Gopnik, Alison (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
• Format: Article
• Language: English
• Published: Wiley Blackwell, 2015-01-12T20:09:45Z.
• Subjects: Article
• Online Access: Get fulltext

 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.