Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence.
Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence.
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DOI：
10.1001/archgenpsychiatry.2011.1269
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发表时间：
2012-02
影响因子：
--
通讯作者：
Frank, Michael J.
中科院分区：
文献类型：
--
作者：
Gold, James M.;Waltz, James A.;Matveeva, Tatyana M.;Kasanova, Zuzana;Strauss, Gregory P.;Herbener, Ellen S.;Collins, Anne G. E.;Frank, Michael J.
阴性症状是精神分裂症的核心特征，但其病理生理机制尚不清楚。阴性症状的定义是缺乏正常功能。然而，必须有一个生产机制，导致这种缺失。在这里，我们测试了一个强化学习账户，表明负面症状是由于未能代表奖励的预期值以及保留的损失避免学习而导致的。受试者进行了概率强化学习范式，涉及刺激对的选择导致奖励或避免损失。训练后，受试者在转移任务中表明他们对刺激的评价。计算模型被用来区分数据的替代账户。三级医疗研究门诊。共有47名临床稳定的精神分裂症或情感障碍患者和28名健康志愿者参加了研究。将患者分为高阴性症状组和低阴性症状组。1)导致奖励或损失避免的选择数量和2）转移阶段的表现。从三个不同的模型的定量拟合进行了检查。高阴性症状患者表现出受损的学习奖励，但完整的损失避免学习，并未能区分奖励刺激和损失避免刺激的转移阶段。模型拟合显示，高阴性症状患者的特点是更好的“演员-评论家”模型，学习刺激-反应协会，而控制和低阴性症状患者纳入他们的行动（“Q-学习”）的预期价值的选择过程。阴性症状与特定的强化学习异常相关：高阴性症状患者在做出决策时并不代表奖励的预期值，而是通过使用预测错误来学习避免惩罚。这种计算框架提供了在机械水平上理解阴性症状的可能性。
Negative symptoms are a core feature of schizophrenia, but their pathophysiology remains unclear. Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence. Here, we test a reinforcement learning account suggesting that negative symptoms result from a failure to represent the expected value of rewards coupled with preserved loss avoidance learning. Subjects performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in either reward or avoidance of loss. Following training, subjects indicated their valuation of the stimuli in a transfer task. Computational modeling was used to distinguish between alternative accounts of the data. A tertiary care research outpatient clinic. A total of 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated. Patients were divided into high and low negative symptom groups. 1) The number of choices leading to reward or loss avoidance and 2) performance in the transfer phase. Quantitative fits from three different models were examined. High negative symptom patients demonstrated impaired learning from rewards but intact loss avoidance learning, and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer phase. Model fits revealed that high negative symptom patients were better characterized by an “actor-critic” model, learning stimulus-response associations, whereas controls and low negative symptom patients incorporated expected value of their actions (“Q-learning”) into the selection process. Negative symptoms are associated with a specific reinforcement learning abnormality: High negative symptoms patients do not represent the expected value of rewards when making decisions but learn to avoid punishments through the use of prediction errors. This computational framework offers the potential to understand negative symptoms at a mechanistic level.
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影响因子：
25
作者：
Maia, Tiago V.;Frank, Michael J.
通讯作者：
Frank, Michael J.
影响因子：
4.1
作者：
Laruelle, M;Abi-Dargham, P
通讯作者：
Abi-Dargham, P
DOI：
10.1093/brain/awm173
发表时间：
2007-09
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Corlett PR;Murray GK;Honey GD;Aitken MR;Shanks DR;Robbins TW;Bullmore ET;Dickinson A;Fletcher PC
通讯作者：
Fletcher PC
影响因子：
25
作者：
Frank, Michael J.;Doll, Bradley B.;Oas-Terpstra, Jen;Moreno, Francisco
通讯作者：
Moreno, Francisco
影响因子：
56.9
作者：
Frank, MJ;Seeberger, LC;O'Reilly, RC
通讯作者：
O'Reilly, RC