Massachusetts Institute of Technology, United States.
Massachusetts Institute of Technology, United States.
Cognition. 2018 Aug;177:122-141. doi: 10.1016/j.cognition.2018.03.019. Epub 2018 May 3.
How do people hold others responsible for the consequences of their actions? We propose a computational model that attributes responsibility as a function of what the observed action reveals about the person, and the causal role that the person's action played in bringing about the outcome. The model first infers what type of person someone is from having observed their action. It then compares a prior expectation of how a person would behave with a posterior expectation after having observed the person's action. The model predicts that a person is blamed for negative outcomes to the extent that the posterior expectation is lower than the prior, and credited for positive outcomes if the posterior is greater than the prior. We model the causal role of a person's action by using a counterfactual model that considers how close the action was to having been pivotal for the outcome. The model captures participants' responsibility judgments to a high degree of quantitative accuracy across three experiments that cover a range of different situations. It also solves an existing puzzle in the literature on the relationship between action expectations and responsibility judgments. Whether an unexpected action yields more or less credit depends on whether the action was diagnostic for good or bad future performance.
人们如何让他人对自己行为的后果负责？我们提出了一个计算模型，将责任归因于观察到的行为所揭示的人的特征，以及人的行为在导致结果中所起的因果作用。该模型首先根据观察到的行为推断出一个人的类型。然后，它将一个人在观察到他们的行为之前的行为预期与观察后的行为预期进行比较。如果后验预期低于先验预期，则模型预测会对负面结果进行责备；如果后验预期大于先验预期，则对正面结果进行奖励。我们通过使用反事实模型来模拟一个人的行为的因果作用，该模型考虑了该行为与结果之间的接近程度。该模型在涵盖各种不同情况的三个实验中，以高度的定量准确性捕捉到了参与者的责任判断。它还解决了关于行为预期和责任判断之间关系的文献中的一个现有难题。出乎意料的行为是否会获得更多或更少的信用，取决于该行为是否对未来的良好或不良表现具有诊断意义。
