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A theory of causal learning in children: Causal maps and Bayes nets

Gopnik, Alison and Glymour, Clark and Sobel, David and Schulz, Laura and Kushnir, Tamar and Danks, David (2002) A theory of causal learning in children: Causal maps and Bayes nets. UNSPECIFIED. (In Press)

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Abstract

We propose that children employ specialized cognitive systems that allow them to recover an accurate causal map of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.


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Item Type: Other
Creators:
CreatorsEmailORCID
Gopnik, Alison
Glymour, Clark
Sobel, David
Schulz, Laura
Kushnir, Tamar
Danks, David
Subjects: General Issues > Causation
Depositing User: Alison Gopnik
Date Deposited: 26 Nov 2002
Last Modified: 13 Sep 2015 15:24
Item ID: 907
Public Domain: No
URI: http://philsci-archive.pitt.edu/id/eprint/907

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