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

Authors
Alison Gopnik
University of California, Berkeley
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
Keywords Bayesian inference  Causal induction  Knowledge effects  Cognitive development
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DOI 10.1111/j.1551-6709.2011.01203.x
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
The Origin of Concepts.Susan Carey - 2009 - Oxford University Press.
Nature's Capacities and Their Measurement.Nancy Cartwright - 1989 - Oxford, England: Oxford University Press.

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Citations of this work BETA

The Cost of Learning: Interference Effects in Memory Development.Kevin P. Darby & Vladimir M. Sloutsky - 2015 - Journal of Experimental Psychology: General 144 (2):410-431.
The Development of Causal Categorization.Brett K. Hayes & Bob Rehder - 2012 - Cognitive Science 36 (6):1102-1128.

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