Abstract：
Counterfactual theories propose that people\'s capacity for causal judgment depends on their ability to consider alternative possibilities: The lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate a variety of psychological effects on causal judgment, a range of recent accounts have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded measure of causal strength. While such models successfully describe the influence of the statistical normality (i.e., the base rate) of the candidate and alternate causes on causal judgments, we show that they make further untested predictions about how normality influences people\'s confidence in their causal judgments. In a large (N = 3,020) sample of participants in a causal judgment task, we found that normality indeed influences people\'s confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model in which people are more confident in a causal relationship when the effect of the cause is less variable among imagined counterfactual possibilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
摘要：
反事实理论认为，人们的因果判断能力取决于他们考虑其他可能性的能力：雷击导致森林火灾，因为它没有击中，森林大火不会随之而来。为了适应因果判断的各种心理影响，最近的一系列报道提出，人们从概率上抽样反事实替代方案，从中计算因果强度的分级度量。虽然这样的模型成功地描述了统计正态的影响(即，基本比率)对因果判断的候选和替代原因，我们表明，他们对正常性如何影响人们对其因果判断的信心做出了进一步未经测试的预测。在因果判断任务的大量(N=3,020)参与者样本中，我们发现，正态确实会影响人们对因果判断的信心，并且这些影响是通过反事实抽样模型预测的，在该模型中，当原因的影响在想象的反事实可能性中变化较小时，人们对因果关系更有信心。（PsycInfo数据库记录（c）2024APA，保留所有权利)。
