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精神分裂症患者的预期价值计算受损与整合近期结果的奖励概率和幅度的能力降低有关。

Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes.

机构信息

Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.

Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island; Department of Psychiatry and Brown Institute for Brain Science, Brown University, Providence, Rhode Island.

出版信息

Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Mar;4(3):280-290. doi: 10.1016/j.bpsc.2018.11.011. Epub 2018 Dec 7.

Abstract

BACKGROUND

Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on "valueless" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.

METHODS

Healthy volunteers (n = 38) and PSZ (n = 49) completed a learning paradigm consisting of four stimulus pairs. Reward magnitude (3, 2, 1, 0 points) and probability (90%, 80%, 20%, 10%) determined each stimulus's EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.

RESULTS

PSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability × magnitude) between two competing stimuli increased. Using a previously validated computational hybrid model, PSZ relied less on EV ("Q-learning") and more on stimulus-response learning ("actor-critic"), which correlated with Scale for the Assessment of Negative Symptoms motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability.

CONCLUSIONS

Probability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on valueless stimulus-response associations. Confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work provides a first mechanistic explanation of complex EV-based learning deficits in PSZ with motivational deficits that arise from an inability to combine information from different reward modalities.

摘要

背景

精神分裂症患者(PSZ)的动机缺陷与无法整合先前结果的大小和概率有关。然而,概率-大小整合缺陷的潜在机制仍知之甚少。我们假设,增加对“无价值”刺激-反应关联的依赖,而不是基于预期价值(EV)的学习,可能会导致具有动机缺陷的 PSZ 出现概率-大小整合缺陷。

方法

健康志愿者(n=38)和 PSZ(n=49)完成了一个由四个刺激对组成的学习范式。奖励大小(3、2、1、0 分)和概率(90%、80%、20%、10%)决定了每个刺激的 EV。在学习阶段之后,呈现新的和熟悉的刺激对。要求参与者选择具有最高奖励价值的刺激。

结果

具有高动机缺陷的 PSZ 随着两个竞争刺激之间的奖励价值(概率×大小)差异的增加,做出的选择越来越不理想。使用先前验证的计算混合模型,PSZ 对 EV(“Q 学习”)的依赖减少,对刺激-反应学习(“行为者-评论家”)的依赖增加,这与评估阴性症状的动机缺陷严重程度的量表相关。PSZ 特别无法表示奖励大小,与模型演示一致,表明行为者-评论家的反应倾向优先受奖励概率驱动。

结论

具有动机缺陷的 PSZ 中的概率-大小缺陷是由于对 EV 的利用不足,而倾向于依赖无价值的刺激-反应关联。我们的计算混合框架证实,概率-大小整合缺陷是由无法表示奖励大小引起的,这是由于无法将不同奖励模态的信息结合起来。

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