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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
    Subjects: General Issues > Causation
    Conferences and Volumes: [2002] Philosophy of Science Assoc. 18th Biennial Mtg - PSA 2002: Contributed Papers (Milwaukee, WI; 2002) > PSA 2002 Workshops
    Depositing User: Alison Gopnik
    Date Deposited: 26 Nov 2002
    Last Modified: 07 Oct 2010 11:11
    Item ID: 907
    Public Domain: No
    URI: http://philsci-archive.pitt.edu/id/eprint/907

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