Cognitive Science 40 (8):2137-2150 (2016)

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism. These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally and appear to be domain-general. Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.
Keywords Structure induction  Causal induction  Bayes nets  Causal learning
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DOI 10.1111/cogs.12318
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.

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The Oxford Handbook of Causal Reasoning.Michael R. Waldmann (ed.) - 2017 - Oxford University Press.

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