Beyond the information given: Causal models in learning and reasoning
Beyond the information given: Causal models in learning and reasoning
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DOI：
10.1111/j.1467-8721.2006.00458.x
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发表时间：
2006-12-01
影响因子：
7.2
通讯作者：
Blaisdell, Aaron P.
中科院分区：
文献类型：
--
作者：
Waldmann, Michael R.;Hagmayer, York;Blaisdell, Aaron P.
哲学家大卫休谟的结论，即因果归纳法完全基于观察到的联系仍然是心理学的一个难题。如果我们仅仅获得了关于观测事件之间统计协变的知识，而没有获得关于因果关系的更深层次的信息，我们将无法理解因果关系和虚假关系之间的差异，预测和诊断之间的差异，以及观察和干预推断之间的差异。所有这些区别都需要对因果关系的深刻理解，而不仅仅是给出的信息。我们报告了一些最近的研究表明，人和老鼠不坚持事件协变的表面水平，但原因和学习的基础上更深层次的因果关系表示。因果模型理论为这种非凡的能力提供了一个统一的解释。
The philosopher David Hume's conclusion that causal induction is solely based on observed associations still presents a puzzle to psychology. If we only acquired knowledge about statistical covariations between observed events without accessing deeper information about causality, we would be unable to understand the differences between causal and spurious relations, between prediction and diagnosis, and between observational and interventional inferences. All these distinctions require a deep understanding of causality that goes beyond the information given. We report a number of recent studies that demonstrate that people and rats do not stick to the superficial level of event covariations but reason and learn on the basis of deeper causal representations. Causal-model theory provides a unified account of this remarkable competence.