A theory of causal learning in children: Causal maps and bayes nets (2004)
| Venue: | Psychological Review |
| Citations: | 95 - 16 self |
BibTeX
@ARTICLE{Gopnik04atheory,
author = {Alison Gopnik and Clark Glymour and David M. Sobel and Laura E. Schulz and Tamar Kushnir and David Danks},
title = {A theory of causal learning in children: Causal maps and bayes nets},
journal = {Psychological Review},
year = {2004},
volume = {111},
pages = {3--32}
}
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Abstract
CAUSAL MAPS 2 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, 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. A theory of causal learning in children: Causal maps and Bayes nets CAUSAL MAPS 3 When we are children, the input that reaches us from the world is concrete, particular, and limited. Yet, as adults, we have abstract, coherent, and largely veridical
