Causal learning in children: Causal maps and Bayes nets
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with them. Preliminary experimental results suggest that 2- to 4-year-old children spontaneously construct new causal maps and that their learning is consistent with the Bayes-Net formalism.
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