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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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    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: 13 Sep 2015 11:24
    Item ID: 907
    Public Domain: No

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