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

A theory of causal learning in children: Causal maps and Bayes nets (2004)

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by Alison Gopnik , Clark Glymour , David M. Sobel , Laura E. Schulz , Tamar Kushnir , David Danks

Venue:	PSYCHOLOGICAL REVIEW
Citations:	95 - 16 self

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Datum	Value	Source
TITLE	A theory of causal learning in children: Causal maps and bayes nets	INFERENCE
AUTHOR NAME	Alison Gopnik	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California, Berkeley	SVM HeaderParse 0.2
AUTHOR NAME	David M. Sobel	SVM HeaderParse 0.2
AUTHOR AFFIL	Brown University	SVM HeaderParse 0.2
AUTHOR NAME	Clark Glymour	SVM HeaderParse 0.2
AUTHOR AFFIL	Carnegie Mellon University and; Institute for Human and Machine Cognition	SVM HeaderParse 0.2
AUTHOR NAME	Laura E. Schulz	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California, Berkeley	SVM HeaderParse 0.2
AUTHOR NAME	Tamar Kushnir	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California, Berkeley	SVM HeaderParse 0.2
AUTHOR NAME	David Danks	SVM HeaderParse 0.2
AUTHOR AFFIL	Carnegie Mellon University and Institute for Human and Machine Cognition	SVM HeaderParse 0.2
AUTHOR NAME	Alison Gopnik	SVM HeaderParse 0.2
AUTHOR AFFIL	Psychology, University of California, Berkeley; Clark Glymour and David; Danks, Department of Philosophy, Carnegie Mellon University, and Institute	SVM HeaderParse 0.2
AUTHOR ADDR	for Human and Machine Cognition, Pensacola, Florida; David M.	SVM HeaderParse 0.2
AUTHOR NAME	Laura E. Schulz	SVM HeaderParse 0.2
AUTHOR AFFIL	Psychology, University of California, Berkeley; Clark Glymour and David; Danks, Department of Philosophy, Carnegie Mellon University, and Institute	SVM HeaderParse 0.2
AUTHOR ADDR	for Human and Machine Cognition, Pensacola, Florida; David M.	SVM HeaderParse 0.2
AUTHOR NAME	Tamar Kushnir	SVM HeaderParse 0.2
AUTHOR AFFIL	Psychology, University of California, Berkeley; Clark Glymour and David; Danks, Department of Philosophy, Carnegie Mellon University, and Institute	SVM HeaderParse 0.2
AUTHOR ADDR	for Human and Machine Cognition, Pensacola, Florida; David M.	SVM HeaderParse 0.2
AUTHOR NAME	Department Of	SVM HeaderParse 0.2
AUTHOR AFFIL	Psychology, University of California, Berkeley; Clark Glymour and David; Danks, Department of Philosophy, Carnegie Mellon University, and Institute	SVM HeaderParse 0.2
AUTHOR ADDR	for Human and Machine Cognition, Pensacola, Florida; David M.	SVM HeaderParse 0.2
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 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism. The input that reaches children from the world is concrete, particular, and limited. Yet, adults have abstract, coherent, and largely veridical representations of the world. The great epistemological question of cognitive development is how human beings get from one place to the other: How do children learn so much about the world so quickly and effortlessly? In the past 30 years, cognitive developmentalists have demonstrated that there are systematic	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	Psychological Review	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	3--32	INFERENCE
VOLUME	111	INFERENCE
CITATIONS	96 found	ParsCit 1.0