Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI.
J Exp Psychol Gen. 2011 May;140(2):168-85. doi: 10.1037/a0022100.
In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way.
在本文中，我们探讨了认知的因果贝叶斯网络理论与基于因果信念的概率判断存在系统性偏差之间的明显矛盾。偏见的一个潜在来源是从原因到结果的推理（预测推理）相对于从结果到原因的推理（诊断推理）的容易程度。我们使用因果贝叶斯网络，为预测和诊断概率判断如何随替代原因、因果效力和先验概率的强度而变化，提供了一种规范的表述。通过两个实验对该模型进行了测试，这两个实验引出了对各种场景的预测和诊断判断，以及对因果参数的判断，这些场景旨在在替代原因的强度方面有所不同。模型预测与诊断判断非常吻合，但预测判断系统地忽略了替代原因，导致拟合度相对较差。另外三项实验提供了更多证据，证明了在预测推理中对替代原因的忽视，并排除了实用主义的解释。我们的结论是，人们以复杂但并非完全正确的方式使用因果结构来生成概率判断。
