Simon Dan, Read Stephen J
Gould School of Law, University of Southern California.
Department of Psychology, University of Southern California.
Perspect Psychol Sci. 2025 May;20(3):421-459. doi: 10.1177/17456916231204579. Epub 2023 Nov 20.
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.
大量的实验研究致力于揭示有偏差的推理形式。尽管偏差研究丰富且总体实证可靠，但该领域可被描述为脱节、不完整且理论不足。在本文中，我们试图通过提供“基于连贯性的推理”来解决这种脱节，这是一个简洁的理论框架，解释了大量与规范性推理形式的重要偏差。基于连贯性的推理以联结主义网络表示，并通过约束满足处理进行加工，它是人们心理工具箱中普遍存在、必不可少且具有压倒性适应性的工具。然而，当网络由不正确、权重过高或其他非规范性的节点或链接主导时，这种适应性过程很容易被偏差所掩盖。我们应用这个框架来解释各种已确立的有偏差的推理形式，包括证实偏差、晕轮效应、刻板印象溢出、后见之明偏差、动机性推理、情感驱动推理、意识形态推理等等。
