人更倾向相信他人行为背后的因果信念。
Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality
- 用准确度、信息量和因果相关性量化信念解释力。
- 因果相关性是预测人们信念归因的最强因素。
- 适合研究认知科学与心理建模的学者阅读。
人类心智理论的核心能力之一是将信念归因于其他代理者，以解释其行为。然而，由于代理者可能持有的信念种类繁多，且表达方式丰富，人们倾向于归因哪些具体信念？本文提出假设：人们更倾向于归因那些能良好解释观察行为的信念。我们构建了一个计算模型，通过三个维度——准确性、信息量和对行为的因果相关性——量化自然语言中信念陈述的解释力，这些维度均可从信念驱动行为的概率生成模型中计算得出。通过实验，参与者观看一个代理者在盒子中寻找钥匙以达成目标的过程，随后对一系列描述该代理者对盒子内容信念的陈述进行排序。结果表明，当准确性和信息量结合时，可较好预测排序结果；但因果相关性是单独解释参与者响应的最佳因素。
原文摘要 · Abstract (English)
A key feature of human theory-of-mind is the ability to attribute beliefs to other agents as mentalistic explanations for their behavior. But given the wide variety of beliefs that agents may hold about the world and the rich language we can use to express them, which specific beliefs are people inclined to attribute to others? In this paper, we investigate the hypothesis that people prefer to attribute beliefs that are good explanations for the behavior they observe. We develop a computational model that quantifies the explanatory strength of a (natural language) statement about an agent's beliefs via three factors: accuracy, informativity, and causal relevance to actions, each of which can be computed from a probabilistic generative model of belief-driven behavior. Using this model, we study the role of each factor in how people selectively attribute beliefs to other agents. We investigate this via an experiment where participants watch an agent collect keys hidden in boxes in order to reach a goal, then rank a set of statements describing the agent's beliefs about the boxes' contents. We find that accuracy and informativity perform reasonably well at predicting these rankings when combined, but that causal relevance is the single factor that best explains participants' responses.
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