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首页> 外文期刊>Journal of Experimental Psychology. General >Asymmetries in Predictive and Diagnostic Reasoning
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Asymmetries in Predictive and Diagnostic Reasoning

机译：预测和诊断推理中的不对称

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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.
机译：在本文中，我们解决了因果贝叶斯网络认知理论之间的明显差异，后者认为因果信念的​​不确定性判断是通过尊重概率规范的方式从因果信念产生的，并证明基于因果信念的​​概率判断是系统地错误。偏见的一个据称来源是从原因到结果的推理（预测性推理）与从结果到原因的后退推理（诊断推理）的难易程度。使用因果贝叶斯网络，我们制定了一个规范性的表述，说明预测性和诊断性概率判断应如何随着替代原因，因果力和先验概率的变化而变化。该模型通过两个实验进行了测试，这些实验得出了预测性和诊断性判断以及因各种方案而设计的因果性不同的因果参数的判断。模型预测与诊断判断非常接近，但是预测判断显示出系统地忽略了其他原因，因此拟合度相对较差。另外三个实验提供了更多证据，表明在预测推理中忽略了替代原因，并排除了务实的解释。我们得出的结论是，人们使用因果结构以复杂但并非完全垂直的方式生成概率判断。

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