Many experiments have shown that humans and other animals can detect contingency between events accurately. This learning is used to make predictions and to infer causal relationships, both of which are critical for survival. Under certain conditions, however, people tend to overestimate a null contingency. We argue that a successful theory of contingency learning should explain both results. The main purpose of the present review is to assess whether cue-outcome associations might provide the common underlying mechanism that would allow us to explain both accurate and biased contingency learning. In addition, we discuss whether associations can also account for causal learning. After providing a brief description on both accurate and biased contingency judgments, we elaborate on the main predictions of associative models and describe some supporting evidence. Then, we discuss a number of findings in the literature that, although conducted with a different purpose and in different areas of research, can also be regarded as supportive of the associative framework. Finally, we discuss some problems with the associative view and discuss some alternative proposals as well as some of the areas of current debate. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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准确和有偏见的应急判断基础的学习机制。

许多实验表明，人类和其他动物可以准确地检测事件之间的偶然性。该学习用于做出预测并推断因果关系，这两者对于生存都是至关重要的。但是，在某些情况下，人们往往会高估无效费用。我们认为，一种成功的偶然性学习理论应该可以解释这两种结果。本综述的主要目的是评估提示结果关联是否可能提供共同的基础机制，使我们能够解释准确的和有偏见的权变学习。此外，我们讨论了联想是否也可以解释因果学习。在对准确和有偏见的应急判断提供简要说明之后，我们阐述了关联模型的主要预测，并描述了一些支持证据。然后，我们讨论了文献中的许多发现，尽管这些发现是出于不同的目的和在不同的研究领域进行的，但它们也可以被视为对关联框架的支持。最后，我们用联系的观点讨论一些问题，并讨论一些替代性建议以及当前辩论的一些领域。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。我们用关联的观点讨论一些问题，并讨论一些替代性建议以及当前辩论的一些领域。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。我们用关联的观点讨论一些问题，并讨论一些替代性建议以及当前辩论的一些领域。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。