Studies of Psychology and Behavior ›› 2026, Vol. 24 ›› Issue (4): 559-568.DOI: 10.12139/j.1672-0628.2026.04.016

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The Cognitive Computational Mechanism and Emotional Regulation of Moral Decision-Making Under Risk Situations

Xiao XIAO1, Youlong ZHAN*,2(), Qingyin XIAO2, Xiaoyang LI2, Wangda HE3   

  1. 1. School of Education, Hunan First Normal University, Changsha 410205
    2. School of Education, Hunan University of Science and Technology, Xiangtan 411201
    3. Honghua Lake Primary School of Hui Cheng District, Huizhou 516008
  • Received:2025-09-05 Online:2026-07-21 Published:2026-07-20
  • Contact: Youlong ZHAN

风险情境下道德决策的认知计算机制及情绪调节

肖啸1, 占友龙*,2(), 肖青茵2, 李晓阳2, 和旺达3   

  1. 1. 湖南第一师范学院教育学院,长沙 410205
    2. 湖南科技大学教育学院,湘潭 411201
    3. 惠州市惠城区红花湖小学,惠州 516008
  • 通讯作者: 占友龙
  • 基金资助:
    湖南省哲学社会科学规划基金项目(22YBA263)。

Abstract:

This study employed a “risky helping task” combined with cognitive computational modeling techniques to investigate the intrinsic cognitive computational mechanisms underlying individual moral decision-making in risky contexts. Through two experiments, the moderating role of emotions (positive/negative) was further explored. Experiment 1 revealed that college students’ behavior in risky helping decisions was simultaneously influenced by two cognitive biases: loss aversion (α parameter) and guilt aversion (β parameter). As the risk of helping failure increased, individuals’ loss aversion intensified while guilt aversion weakened, leading to a decrease in the subjective value (ΔSV) assessment of risky helping options and a reduction in helping behavior. The turning point for risk aversion occurred at a 30% risk level. Experiment 2 further found that, compared to the neutral emotion group (risk aversion turning point at 40%), the positive emotion group exhibited a delayed risk aversion turning point at 60%, with significantly reduced loss aversion and guilt aversion. Moreover, they assigned higher subjective value to risky helping options and ultimately made more helping choices. The negative emotion group showed stronger loss aversion, assigned lower subjective value, and made less helpful choices compared with the neutral group. The results indicate that moral decision-making in risky contexts follows an integrated “loss-guilt aversion” processing model, where the risk of helping failure suppresses helping behavior by enhancing loss aversion, while positive emotions can effectively buffer the negative impact of risk. This study provides computational evidence for understanding the cognitive processes of moral decision-making and the role of emotional interventions.

Key words: moral decision-making, loss aversion, guilt aversion, emotion, cognitive computational modeling

摘要:

本研究采用“有风险的助人任务”与认知计算建模技术,通过两个实验考察了风险情境下个体道德决策的内在认知计算机制,并进一步探讨了情绪(积极/消极)的调节作用。实验1发现,大学生在风险助人决策时,其行为同时受到损失厌恶与内疚厌恶两种认知偏好的影响;随着助人失败风险升高,个体的损失厌恶倾向增强,内疚厌恶倾向减弱,导致对风险助人选项的主观价值评估降低,助人行为减少,且风险厌恶的转折点出现在30%的风险水平。实验2在此基础上引入情绪操纵,发现与中性情绪组(风险厌恶转折点在40%)相比,积极情绪组被试的风险厌恶转折点延迟至60%,其损失厌恶和内疚厌恶倾向均显著降低,对风险助人选项的主观价值更高,最终做出更多的助人选择;而消极情绪组的损失厌恶显著更强,对风险助人选项的主观价值更低,并做出更少的助人选择。结果表明,风险情境下的道德决策遵循“损失−内疚厌恶”整合加工模型,助人失败风险通过增强损失厌恶抑制助人行为,而积极情绪能有效缓冲风险带来的消极影响。本研究为理解道德决策的认知过程及情绪干预提供了计算层面的证据。

关键词: 道德决策, 损失厌恶, 内疚厌恶, 情绪, 认知计算建模

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