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A computational framework for understanding the roles of simplicity and rational support in people's behavior explanations
Cognition ( IF 4.011 ) Pub Date : 2021-02-08 , DOI: 10.1016/j.cognition.2021.104606
Alan Jern 1 , Austin Derrow-Pinion 2 , A J Piergiovanni 3
Affiliation  

When explaining other people's behavior, people generally find some explanations more satisfying than others. We propose that people judge behavior explanations based on two computational principles: simplicity and rational support—the extent to which an explanation makes the behavior “make sense” under the assumption that the person is a rational agent. Furthermore, we present a computational framework based on decision networks that can formalize both of these principles. We tested this account in a series of experiments in which subjects rated or generated explanations for other people's behavior. In Experiments 1 and 2, the explanations varied in what the other person liked and disliked. In Experiment 3, the explanations varied in what the other person knew or believed. Results from Experiments 1 and 2 supported the idea that people rely on both simplicity and rational support. However, Experiment 3 suggested that subjects rely only on rational support when judging explanations of people's behavior that vary in what someone knew.



中文翻译:

一个用于理解简单性和合理支持在人们行为解释中的作用的计算框架

在解释别人的行为时,人们通常会发现一些比别人更满意的解释。我们建议人们根据两个计算原理来判断行为解释:简单性和理性支持-在假设人是理性主体的假设下,解释使行为“有意义”的程度。此外,我们提出了一个基于决策网络的计算框架,可以将这两个原则形式化。我们在一系列实验中测试了此帐户,在这些实验中,受试者对其他人的行为进行了评分或给出了解释。在实验1和2中,对其他人喜欢和不喜欢的东西的解释各不相同。在实验3中,其他人的了解或相信的解释各不相同。实验1和2的结果支持人们依赖简单和理性支持的观点。但是,实验3建议受试者在判断对人们行为的理解时,只能依靠理性的支持,因为人们的行为有所不同。

更新日期:2021-02-08
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