Gold James M, Waltz James A, Matveeva Tatyana M, Kasanova Zuzana, Strauss Gregory P, Herbener Ellen S, Collins Anne G E, Frank Michael J
Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD 21228, USA.
Arch Gen Psychiatry. 2012 Feb;69(2):129-38. doi: 10.1001/archgenpsychiatry.2011.1269.
Negative symptoms are a core feature of schizophrenia, but their pathogenesis remains unclear. Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence.
To test a reinforcement learning account suggesting that negative symptoms result from a failure in the representation of the expected value of rewards coupled with preserved loss-avoidance learning.
Participants performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in reward or in loss avoidance. Following training, participants indicated their valuation of the stimuli in a transfer test phase. Computational modeling was used to distinguish between alternative accounts of the data.
A tertiary care research outpatient clinic.
In total, 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated in the study. Patients were divided into a high-negative symptom group and a low-negative symptom group.
The number of choices leading to reward or loss avoidance, as well as performance in the transfer test phase. Quantitative fits from 3 different models were examined.
Patients in the high-negative symptom group demonstrated impaired learning from rewards but intact loss-avoidance learning and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer test phase. Model fits revealed that patients in the high-negative symptom group were better characterized by an "actor-critic" model, learning stimulus-response associations, whereas control subjects and patients in the low-negative symptom group incorporated expected value of their actions ("Q learning") into the selection process.
Negative symptoms in schizophrenia are associated with a specific reinforcement learning abnormality: patients with high-negative symptoms 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.
阴性症状是精神分裂症的核心特征，但其发病机制仍不清楚。阴性症状是由正常功能的缺失所定义的。然而，必然存在一种导致这种缺失的产生机制。
检验一种强化学习理论，该理论认为阴性症状是由于奖励预期值表征失败以及损失规避学习保留所致。
参与者进行了一种概率强化学习范式，涉及刺激对，其中选择会导致奖励或损失规避。训练后，参与者在转移测试阶段表明他们对刺激的评估。使用计算模型来区分对数据的不同解释。
一家三级护理研究门诊诊所。
共有47名临床稳定的精神分裂症或分裂情感性障碍患者以及28名健康志愿者参与了该研究。患者被分为高阴性症状组和低阴性症状组。
导致奖励或损失规避的选择数量，以及转移测试阶段的表现。检查了来自3种不同模型的定量拟合。
高阴性症状组患者在从奖励中学习方面受损，但损失规避学习完好，并且在转移测试阶段未能区分奖励性刺激和损失规避性刺激。模型拟合显示，高阴性症状组患者更适合用“行动者-评论家”模型来表征，即学习刺激-反应关联，而对照组受试者和低阴性症状组患者在选择过程中纳入了其行动的预期值（“Q学习”）。
精神分裂症的阴性症状与一种特定的强化学习异常有关：高阴性症状患者在做决策时不表征奖励的预期值，但通过利用预测误差来学习避免惩罚。这个计算框架为在机制层面理解阴性症状提供了可能性。
