Causal learning about tolerance and sensitization.
Causal learning about tolerance and sensitization.
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DOI：
10.3758/pbr.16.6.1043
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发表时间：
2009-12
影响因子：
3.5
通讯作者：
Ahn WK
中科院分区：
文献类型：
--
作者：
Rottman BM;Ahn WK
我们介绍了因果学习过程中使用的两个抽象的因果图式。 (1) 耐受性是指效应随着时间的推移而减弱，因为实体反复暴露于原因（例如，一个人对咖啡因变得耐受）。 (2) 敏化是指效应随着时间的推移而增强，因为实体反复暴露于原因（例如，抗抑郁药通过重复使用变得更有效）。在实验 1 中，参与者观察到这些因果数据模式随时间的推移而展开，并表现出耐受性或敏化模式。与相同数据随时间随机出现的情况相比，参与者推断出了更强的因果效力，并对新病例做出了更自信、更极端的预测。在实验 2 中，相同的耐受/敏化场景发生在一个实体内或多个实体之间。在多实体条件下，当图式被违反时，参与者做出的推论要弱得多。讨论了因果学习的含义。
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.