The question of how people hold others responsible has motivated decades of theorizing and empirical work. In this paper, we develop and test a computational model that bridges the gap between broad but qualitative framework theories, and quantitative but narrow models. In our model, responsibility judgments are the result of two cognitive processes: a dispositional inference about a person’s character from their action, and a causal attribution about the person’s role in bringing about the outcome. We test the model in a group setting in which political committee members vote on whether or not a policy should be passed. We assessed participants’ dispositional inferences and causal attributions by asking how surprising and important a committee member’s vote was. Participants’ answers to these questions in Experiment 1 accurately predicted responsibility judgments in Experiment 2. In Experiments 3 and 4, we show that the model also predicts moral responsibility judgments, and that importance matters more for responsibility, while surprise matters more for judgments of wrongfulness.

人们如何让他人负责的问题激发了数十年的理论和实证工作。在本文中，我们开发并测试了一个计算模型，该模型弥合了广泛但定性的框架理论与定量但狭隘的模型之间的差距。在我们的模型中，责任判断是两个认知过程的结果：从一个人的行为中推断出一个人的性格，以及一个关于这个人在导致结果中的角色的因果归因。我们在政治委员会成员投票决定是否应该通过政策的群体环境中测试该模型。我们通过询问委员会成员的投票有多令人惊讶和重要来评估参与者的性格推断和因果归因。