Learning causes: Psychological explanations of causal explanation
Learning causes: Psychological explanations of causal explanation
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DOI：
10.1023/a:1008234330618
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发表时间：
1998-02-01
影响因子：
7.4
通讯作者：
Glymour, C
中科院分区：
文献类型：
--
作者：
Glymour, C
我认为，对人类因果判断感兴趣的心理学家应该理解并采用一种有向图来表示因果机制，这种有向图编码条件独立性（筛选）关系。我通过(I)展示心理学中关于因果关系的“机械论”和“联想论”心理学理论之间的争论建立在一个错误和混乱的二分法之上，来说明这种表述的好处，它现在被广泛应用于计算机科学和越来越多的统计学中；（ii）表明最近一项被广泛引用的实验，旨在表明人类受试者错误地让大原因“掩盖”了小原因，歪曲了最可能的、最可靠的、对受试者可用的因果解释，根据这些解释，他们的反应是规范的；（iii）展示最近一个关于人类判断因果能力的心理学理论（由P. Cheng提出）如何可以相当广泛地推广；（iv）提出一系列可能的实验，比较人类和计算机从关联中提取因果信息的能力。
I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between 'mechanist' and 'associationist' psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes 'overshadow' small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.