A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Теория каузального обучения у детей: каузальные карты и байесовские сети
2004-01-01
SCID: 54.1/5e8kw7xw
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Abstract (AI)
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.
Key Findings
1
Children may use computational processes resembling Bayesian-network structure learning and causal prediction.
2
Children’s causal knowledge can be formally represented using directed graphical causal models, or Bayesian networks.
3
Experimental evidence indicates that children aged 2–4 construct new causal maps during learning.
4
The learning behavior of 2–4-year-old children is consistent with the formalism of Bayesian networks.
5
The paper proposes a cognitive and computational theory in which children learn abstract, coherent causal maps of relations among events.
Research Object
Causal learning in 2- to 4-year-old children
Research Subject
Children’s construction, learning, and use of causal maps represented as causal Bayes nets for causal inference and prediction
Publication Details
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2004-01-01
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