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

@article{citeulike:432083, 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.}, address = {Department of Psychology, University of California, Berkeley, CA 94720, USA. gopnik@socrates.berkeley.edu}, author = {Gopnik, A. and Glymour, C. and Sobel, D. M. and Schulz, L. E. and Kushnir, T. and Danks, D. }, citeulike-article-id = {432083}, doi = {10.1037/0033-295X.111.1.3}, issn = {0033-295X}, journal = {Psychol Rev}, keywords = {bayesian, causality, child, learning, model, review}, month = {January}, number = {1}, pages = {3--32}, posted-at = {2008-02-12 21:16:47}, priority = {5}, title = {A theory of causal learning in children: causal maps and Bayes nets.}, url = {http://dx.doi.org/10.1037/0033-295X.111.1.3}, volume = {111}, year = {2004} }

TY - JOUR ID - citeulike:432083 L3 - citeulike-article-id:432083 TI - A theory of causal learning in children: causal maps and Bayes nets. JF - Psychol Rev VL - 111 IS - 1 SP - 3 EP - 32 AD - Department of Psychology, University of California, Berkeley, CA 94720, USA. gopnik@socrates.berkeley.edu SN - 0033-295X N2 - 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. KW - bayesian KW - causality KW - child KW - learning KW - model KW - review AU - Gopnik, A AU - Glymour, C AU - Sobel, DM AU - Schulz, LE AU - Kushnir, T AU - Danks, D PY - 2004/01// UR - http://dx.doi.org/10.1037/0033-295X.111.1.3 ER -