Quantinuum, QBA, Centre for Educational Neuroscience, London, United Kingdom.
Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island, United States of America.
PLoS One. 2022 May 13;17(5):e0268219. doi: 10.1371/journal.pone.0268219. eCollection 2022.
Unobservable mechanisms that tie causes to their effects generate observable events. How can one make inferences about hidden causal structures? This paper introduces the domain-matching heuristic to explain how humans perform causal reasoning when lacking mechanistic knowledge. We posit that people reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones. When thinking about a cause, people tend to think about possible effects that participate in the same domain, and vice versa. To explore the specific domains that people use, we asked people to cluster artifacts. The analyses revealed three commonly employed mechanism domains: the mechanical, chemical, and electromagnetic. Using these domains, we tested the domain-matching heuristic by testing adults' and children's causal attribution, prediction, judgment, and subjective understanding. We found that people's responses conform with domain-matching. These results provide evidence for a heuristic that explains how people engage in causal reasoning without directly appealing to mechanistic or probabilistic knowledge.
不可观测的机制将原因与其效应联系起来，从而产生可观测的事件。人们如何对隐藏的因果结构进行推理？本文引入了匹配域启发式来解释当人们缺乏机制知识时如何进行因果推理。我们假设，人们通过只关注最有可能的关系来减少原本广阔的可能因果关系空间。当思考一个原因时，人们往往会思考可能参与同一领域的效应，反之亦然。为了探索人们使用的特定领域，我们要求人们对人工制品进行聚类。分析结果揭示了人们常用的三个机制领域：机械、化学和电磁。利用这些领域，我们通过测试成人和儿童的因果归因、预测、判断和主观理解来检验匹配域启发式。我们发现人们的反应符合匹配域的要求。这些结果为一种启发式提供了证据，该启发式解释了人们如何在不直接诉诸机制或概率知识的情况下进行因果推理。
