A theory of causal learning in children: Causal maps and Bayes nets
A theory of causal learning in children: Causal maps and Bayes nets
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DOI：
10.1037/0033-295x.111.1.3
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发表时间：
2004-01-01
影响因子：
5.4
通讯作者：
Danks, D
中科院分区：
文献类型：
--
作者：
Gopnik, A;Glymour, C;Danks, D
作者概述了儿童因果学习的认知和计算解释。他们提出，儿童使用专门的认知系统，使他们能够恢复准确的世界“因果关系图”：事件之间因果关系的抽象、连贯和习得的表示。这类知识可以通过有向图因果模型或贝叶斯网的形式主义来清楚地理解。儿童的因果学习和推理可能涉及类似于学习因果贝叶斯网络和使用它们进行预测的计算。实验结果表明，2-4岁儿童构建了新的因果地图，他们的学习符合贝叶斯网络形式主义。
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.