title: A theory of causal learning in children: causal maps and Bayes nets.
abstract: The authors outline a cognitive and computational account of causal learning in children. They propose that children use 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.
authors.affiliation: Department of Psychology, University of California, Berkeley, CA 94720, USA. gopnik@socrates.berkeley.edu
authors.forename: Alison
authors.initials: A
authors.lastname: Gopnik
authors.forename: Clark
authors.initials: C
authors.lastname: Glymour
authors.forename: David M
authors.initials: DM
authors.lastname: Sobel
authors.forename: Laura E
authors.initials: LE
authors.lastname: Schulz
authors.forename: Tamar
authors.initials: T
authors.lastname: Kushnir
authors.forename: David
authors.initials: D
authors.lastname: Danks
mesh_terms: D000328:Adult; D001245:Association Learning; D001499:Bayes Theorem; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009039:Motion Perception; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011340:Problem Solving