Korteling Johan E, Brouwer Anne-Marie, Toet Alexander
TNO Human Factors, Soesterberg, Netherlands.
Front Psychol. 2018 Sep 3;9:1561. doi: 10.3389/fpsyg.2018.01561. eCollection 2018.
Human decision-making shows systematic simplifications and deviations from the tenets of rationality ('heuristics') that may lead to suboptimal decisional outcomes ('cognitive biases'). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic ('Type 1') decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. To substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility, (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions, and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena.
人类决策表现出系统性的简化以及与理性原则（“启发法”）的偏差，这可能导致次优决策结果（“认知偏差”）。目前，关于启发法和认知偏差的起源存在三种主流理论观点：认知心理学观点、生态学观点和进化观点。然而，这些观点主要是描述性的，没有一个能为认知偏差的潜在机制提供一个全面的解释框架。为了加深我们对认知启发法和偏差的理解，我们提出了一个认知偏差的神经网络框架，该框架解释了为什么我们的大脑会系统性地倾向于默认采用启发式（“类型1”）决策。我们认为，许多认知偏差源于生物神经网络运作所必需的内在大脑机制。为了证实我们的观点，我们识别并解释了四个基本的神经网络原则：（1）关联，（2）兼容性，（3）保留，以及（4）聚焦。这些原则是（所有）神经网络所固有的，这些神经网络最初是为执行具体的生物、感知和运动功能而优化的。它们构成了我们关联和组合（不相关）信息、优先处理与我们当前状态（如知识、观点和期望）相符的信息、保留有时最好忽略的给定信息，以及关注主导信息而忽略未被直接激活的相关信息的倾向的基础。这些假定的机制是互补的，并非相互排斥的。对于不同的认知偏差，它们可能都在不同程度上导致信息扭曲。当前的观点不仅补充了之前的三种观点，还为许多认知偏差现象提供了一个统一且有约束力的框架。
