Gopnik Alison, Glymour Clark, Sobel David M, Schulz Laura E, Kushnir Tamar, Danks David
Department of Psychology, University of California, Berkeley, CA 94720, USA.
Psychol Rev. 2004 Jan;111(1):3-32. doi: 10.1037/0033-295X.111.1.3.
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.
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，使他们能够恢复世界的准确“因果地图”：一种关于事件间因果关系的抽象、连贯的习得表征。这种知识可以通过有向图形因果模型或贝叶斯网络的形式主义得到清晰理解。儿童的因果学习和推理可能涉及与学习因果贝叶斯网络并据此进行预测类似的计算。实验结果表明，2至4岁的儿童构建了新的因果地图，且他们的学习与贝叶斯网络形式主义相一致。
