反转学习的认知神经基础及在精神病理学中的应用  

The Cognitive and Neural Mechanisms of Human Reversal Learning and Their Applications in Psychopathology

作　　者：相婕 冯廷勇 Xiang Jie;Feng Tingyong(Faculty of Psychology,Southwest University,Chongqing,400715)

机构地区：[1]西南大学心理学部,重庆400715

出　　处：《心理科学》2025年第2期295-305,共11页Journal of Psychological Science

基　　金：国家自然科学基金面上项目(32271123);重庆市技术创新应用发展重点项目(CSTB2022TIAD-KPX0150);西南大学创新研究2035先导计划(SWUPilotPlan006)的资助。

摘　　要：反转学习是一种反映认知灵活性的关键认知能力。当前研究结合强化学习模型和脑影像研究,探讨了反转学习的认知神经基础及临床应用。研究发现,强化学习模型将反转学习分解为决策、反馈和学习(即根据反馈来调整后续行为)三个认知过程,并提供了精细化的认知计算模型。脑影像研究表明,决策过程由额顶控制网络主导;正反馈主要激活奖赏系统,负反馈则激活额顶控制网络(认知控制和注意调节)及情感加工网络等;学习过程涉及前额叶-扣带回网络。反转学习广泛应用于精神病理学研究,为理解注意缺陷多动障碍、抑郁症、强迫症等疾病提供了新视角。未来研究可通过完善认知计算模型和神经计算模型,以进一步提升反转学习的理论深度和应用广度。In today's rapidly changing environment,cognitive and behavioral flexibility are becoming increasingly crucial.Reversal learning refers to the ability to adjust previously learned responses or strategies in the face of environmental changes or new rules,reflecting an individual's cognitive or behavioral flexibility.The reversal learning paradigm,originally applied in animal studies and later extended to human research,is widely used to assess cognitive flexibility.In a classic reversal learning paradigm,participants select between two stimuli to receive a reward;after a reversal of outcomes,they must adjust their choice.This process can be further complicated by probabilistic reversal learning to probe adaptability to changes.Despite numerous studies exploring the cognitive mechanisms,neural bases,and applications of reversal learning in the field of psychopathology,systematic reviews focusing on the cognitive and neural foundations of reversal learning and its applications remain scarce.This study aims to combine cognitive computational modeling and MRI research to comprehensively examine the cognitive processing models and neural mechanisms underlying reversal learning.It further analyzes the applications of reversal learning in psychopathology,with the goal of promoting the flexible use of reversal learning in future research and providing theoretical support for psychopathological studies.The reinforcement learning models(RLM)allow for a nuanced analysis of the cognitive processes involved in reversal learning.These models divide the reversal learning process into decision-making,feedback reception,and learning stages,and provide corresponding computational metrics for each stage,such as value estimation(Q value),decision bias(P value),and decision stability(βvalue)during the decision-making stage;feedback sensitivity(ρ),feedback strength(R),feedback valence,and prediction error(PE)during feedback reception;and learning rate(α)in the learning stage.The decision-making process in reversal learning is primar

关 键 词：反转学习 灵活性 强化学习模型 磁共振成像(MRI) 

分 类 号：R749[医药卫生—神经病学与精神病学]