Center for Evolutionary Psychology, Department of Psychological & Brain Sciences, University of California Santa Barbara, 93106 Santa Barbara, CA, USA.
Cognition. 2020 Dec;205:104410. doi: 10.1016/j.cognition.2020.104410. Epub 2020 Aug 4.
When judging what caused an event, people do not treat all factors equally - for instance, they will say that a forest fire was caused by a lit match, and not mention the oxygen in the air which helped fuel the fire. We develop a computational model formalizing the idea that causal judgment is designed to identify "portable" causes - causes that are likely to generalize across a variety of background circumstances. Under minimal assumptions, the model is surprisingly simple: a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal judgment is influenced by the normality of candidate causes, and outperforms other known computational models when tested against an existing fine-grained dataset of human graded causal judgments (Morris, A., Phillips, J., Gerstenberg, T., & Cushman, F. (2019). Quantitative causal selection patterns in token causation. PloS one, 14(8).).
当判断一个事件的原因时，人们不会平等对待所有因素——例如，他们会说森林火灾是由点燃的火柴引起的，而不会提到空气中有助于火势蔓延的氧气。我们开发了一种计算模型，将因果判断的思想形式化，即因果判断旨在识别“可移植”的原因——这些原因可能在各种背景情况下普遍存在。在最小的假设下，该模型非常简单：一个因素被认为是一个结果的原因，在反事实世界中，该因素与该结果相关。该模型解释了为什么因果判断会受到候选原因的正常性的影响，并且在与现有的人类分级因果判断的细粒度数据集进行测试时，该模型的表现优于其他已知的计算模型（Morris，A.，Phillips，J.，Gerstenberg，T.，& Cushman，F.（2019）。在代币因果关系中，定量因果选择模式。公共科学图书馆·综合，14（8））。
