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

Cognitive Science 35 (8):1407-1455 (2011)
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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

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Author Profiles

Alison Gopnik
University of California, Berkeley
Tom Griffiths
Aarhus University

References found in this work

A treatise of human nature.David Hume & A. D. Lindsay - 1739 - Oxford,: Clarendon press. Edited by L. A. Selby-Bigge.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
The origin of concepts.Susan Carey - 2009 - New York: Oxford University Press.
Nature's capacities and their measurement.Nancy Cartwright - 1989 - New York: Oxford University Press.

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