Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets Alison Gopnik
 

Summary: A Theory of Causal Learning in Children: Causal Maps and Bayes Nets
Alison Gopnik
University of California, Berkeley
Clark Glymour
Carnegie Mellon University and
Institute for Human and Machine Cognition
David M. Sobel
Brown University
Laura E. Schulz and Tamar Kushnir
University of California, Berkeley
David Danks
Carnegie Mellon University and Institute for Human and Machine Cognition
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.

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics