Department of Psychology, Yale University, New Haven, Connecticut 06520-8205, USA.
Psychon Bull Rev. 2009 Dec;16(6):1043-9. doi: 10.3758/PBR.16.6.1043.
We introduce two abstract, causal schemata used during causal learning. (1) Tolerance is when an effect diminishes over time, as an entity is repeatedly exposed to the cause (e.g., a person becoming tolerant to caffeine). (2) Sensitization is when an effect intensifies over time, as an entity is repeatedly exposed to the cause (e.g., an antidepressant becoming more effective through repeated use). In Experiment 1, participants observed either of these cause-effect data patterns unfolding over time and exhibiting the tolerance or sensitization schemata. Participants inferred stronger causal efficacy and made more confident and more extreme predictions about novel cases than in a condition with the same data appearing in a random order over time. In Experiment 2, the same tolerance/sensitization scenarios occurred either within one entity or across many entities. In the many-entity conditions, when the schemata were violated, participants made much weaker inferences. Implications for causal learning are discussed.
我们介绍了两种在因果学习中使用的抽象、因果图式。（1）当一种效应随着时间的推移而减弱，因为实体被反复暴露于原因之下（例如，一个人对咖啡因的耐受性增加），这就是耐受。（2）当一种效应随着时间的推移而增强，因为实体被反复暴露于原因之下（例如，通过重复使用，抗抑郁药变得更有效），这就是敏感化。在实验 1 中，参与者观察了这些因果数据模式中的任何一种随着时间的推移展开，并表现出耐受或敏感化模式。与在数据随时间随机出现的条件相比，参与者对新案例做出了更强的因果效力推断，并做出了更自信和更极端的预测。在实验 2 中，相同的耐受/敏感化场景要么在一个实体内部发生，要么在多个实体之间发生。在多实体条件下，当模式被违反时，参与者做出的推断要弱得多。讨论了对因果学习的影响。
