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第三方惩罚行为的认知神经机制

The cognitive and neural mechanism of third-party punishment

摘要: 第三方惩罚(third-party punishment, TPP)指个体作为第三方或者观察者为维护社会规范对违规者所实施的惩罚行为。大量研究为揭示TPP行为的神经机制提供了启示,但鲜有研究关注不同功能性脑网络在其中发挥的整体作用。本文综述了近十年来TPP相关的研究,对相关理论模型和脑网络进行总结,并在此基础上提出TPP的认知神经网络模型,系统地对TPP行为背后的神经机制进行解释和整合。在该模型中,情绪系统和奖赏系统是TPP的动力来源,认知系统主要负责责任评估以及惩罚的选择;奖赏网络、突显网络、默认模式网络和中央执行网络分别参与不同认知加工阶段。该模型建立了TPP相关研究在心理层面和认知神经层面上的联系,对TPP行为的发生和发展机制进行了更加整体、全面的解释。未来可以引入元分析或基于机器学习的分析方法,在不同的背景信息和更加复杂的社交情境下探讨第三方干预偏好以及背后的认知神经机制。

Abstract: Third-party punishment (TPP) refers to the punitive behaviors that individuals impose on violators as third parties or observers in order to uphold social norms. Many studies have provided insight into the neural mechanisms underlying TPP behavior. However, few studies have focused on the overall role of functional brain networks. This paper reviews the researches related to TPP in the past decade and summarizes the relevant theoretical models and brain networks. Based on the previous studies, we propose a cognitive neural network model of TPP, which could systematically explain and integrate the neural mechanisms behind TPP behavior. In this model, the affective and reward systems are the TPP power sources, and the cognitive system is mainly responsible for responsibility assessment as well as punishment selection. The reward network, the salient network, the default mode network and the central executive network are involved in different cognitive processing stages, respectively. The model establishes the connection between TPP behavior-related research at the psychological and the cognitive-neural level and provides a more holistic and comprehensive explanation of the mechanisms of TPP behavior. In the future, it is necessary to use meta-analysis or machine learning algorisms in order to explore third-party intervention preferences and the underlying cognitive neural mechanisms in different contextual information and more complex social contexts.

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[V1] 2023-09-13 09:12:55 ChinaXiv:202309.00126V1 下载全文
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  • 运营单位: 中国科学院文献情报中心
  • 制作维护:中国科学院文献情报中心知识系统部
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