A considerable amount of experimental research has been devoted to uncovering biased forms of reasoning. Notwithstanding the richness and overall empirical soundness of the bias research, the field can be described as disjointed, incomplete, and undertheorized. In this article, we seek to address this disconnect by offering "coherence-based reasoning" as a parsimonious theoretical framework that explains a sizable number of important deviations from normative forms of reasoning. Represented in connectionist networks and processed through constraint-satisfaction processing, coherence-based reasoning serves as a ubiquitous, essential, and overwhelmingly adaptive apparatus in people's mental toolbox. This adaptive process, however, can readily be overrun by bias when the network is dominated by nodes or links that are incorrect, overweighted, or otherwise nonnormative. We apply this framework to explain a variety of well-established biased forms of reasoning, including confirmation bias, the halo effect, stereotype spillovers, hindsight bias, motivated reasoning, emotion-driven reasoning, ideological reasoning, and more.

中文翻译：

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走向有偏见推理的一般框架：基于连贯性的推理。

大量的实验研究致力于揭示有偏见的推理形式。尽管偏见研究丰富且整体经验健全，但该领域可以被描述为脱节、不完整和理论化不足。在本文中，我们试图通过提供“基于连贯性的推理”作为一种简约的理论框架来解决这种脱节问题，该框架解释了与规范推理形式的大量重要偏差。基于连贯性的推理以联结主义网络为代表，并通过约束满足处理进行处理，是人们心理工具箱中普遍存在的、必不可少的、具有压倒性适应性的装置。然而，当网络由不正确、权重过重或其他不规范的节点或链接主导时，这种自适应过程很容易因偏差而超限。我们应用这个框架来解释各种行之有效的有偏见的推理形式，包括确认偏见、光环效应、刻板印象溢出、后见之明偏见、动机推理、情感驱动推理、意识形态推理等等。