Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World
Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World
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因果不变性是创建世界因果表示的基本约束
DOI：
10.1093/oxfordhb/9780199399550.013.9
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发表时间：
2017
影响因子：
5.7
通讯作者：
Hongjing Lu
中科院分区：
文献类型：
--
作者：
P. Cheng;Hongjing Lu
本章阐述了对世界的因果理解的表征性质，并探讨了其对因果学习的影响。考虑到问题的表征方面，因果关系的搜索空间的浩瀚意味着强大的约束对于达到适应性因果关系是必不可少的。本章回顾了(1)为什么因果不变性-因果机制在不同背景下运作的一致性-是直觉推理中因果学习的基本约束，(2)假设因果不变性为可撤销默认的心理学因果学习理论，(3)因果不变性在因果学习中的计算作用可能变得模糊的一些方式，以及(4)因果不变性作为一般愿望，默认假设的作用，假设修正的标准，以及特定领域的描述。本章还回顾了人类和非人类因果和联想学习文献中令人困惑的差异，并提供了一个潜在的解释。
This chapter illustrates the representational nature of causal understanding of the world and examines its implications for causal learning. The vastness of the search space of causal relations, given the representational aspect of the problem, implies that powerful constraints are essential for arriving at adaptive causal relations. The chapter reviews (1) why causal invariance—the sameness of how a causal mechanism operates across contexts—is an essential constraint for causal learning in intuitive reasoning,(2) a psychological causal-learning theory that assumes causal invariance as a defeasible default,(3) some ways in which the computational role of causal invariance in causal learning can become obscured, and (4) the roles of causal invariance as a general aspiration, a default assumption, a criterion for hypothesis revision, and a domain-specific description. The chapter also reviews a puzzling discrepancy in the human and non-human causal and associative learning literatures and offers a potential explanation.