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快感缺失和抑郁症状背后的焦虑对基于奖励的决策有不同影响。

Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making.

作者信息

Harlé Katia M, Guo Dalin, Zhang Shunan, Paulus Martin P, Yu Angela J

机构信息

Department of Psychiatry, UCSD, La Jolla, CA, United States of America.

Department of Electrical and Computer Engineering, UCSD, La Jolla, CA, United States of America.

出版信息

PLoS One. 2017 Oct 23;12(10):e0186473. doi: 10.1371/journal.pone.0186473. eCollection 2017.

Abstract

Depressive pathology, which includes both heightened negative affect (e.g., anxiety) and reduced positive affect (e.g., anhedonia), is known to be associated with sub-optimal decision-making, particularly in uncertain environments. Here, we use a computational approach to quantify and disambiguate how individual differences in these affective measures specifically relate to different aspects of learning and decision-making in reward-based choice behavior. Fifty-three individuals with a range of depressed mood completed a two-armed bandit task, in which they choose between two arms with fixed but unknown reward rates. The decision-making component, which chooses among options based on current expectations about reward rates, is modeled by two different decision policies: a learning-independent Win-stay/Lose-shift (WSLS) policy that ignores all previous experiences except the last trial, and Softmax, which prefers the arm with the higher expected reward. To model the learning component for the Softmax choice policy, we use a Bayesian inference model, which updates estimated reward rates based on the observed history of trial outcomes. Softmax with Bayesian learning better fits the behavior of 55% of the participants, while the others are better fit by a learning-independent WSLS strategy. Among Softmax "users", those with higher anhedonia are less likely to choose the option estimated to be most rewarding. Moreover, the Softmax parameter mediates the inverse relationship between anhedonia and overall monetary gains. On the other hand, among WSLS "users", higher state anxiety correlates with increasingly better ability of WSLS, relative to Softmax, to explain subjects' trial-by-trial choices. In summary, there is significant variability among individuals in their reward-based, exploratory decision-making, and this variability is at least partly mediated in a very specific manner by affective attributes, such as hedonic tone and state anxiety.

摘要

抑郁病理状态,包括增强的负面情绪(如焦虑)和减弱的正面情绪(如快感缺失),已知与次优决策有关,尤其是在不确定的环境中。在此,我们采用一种计算方法来量化并厘清这些情感指标中的个体差异如何具体与基于奖励的选择行为中学习和决策的不同方面相关联。五十三名情绪抑郁程度各异的个体完成了一项双臂赌博任务,在该任务中他们要在两条奖励率固定但未知的臂之间进行选择。决策部分基于对奖励率的当前预期在各选项中进行选择,由两种不同的决策策略建模:一种不依赖学习的赢留/输换(WSLS)策略,该策略忽略除最后一次试验之外的所有先前经历;以及Softmax策略,该策略更倾向于预期奖励更高的臂。为了对Softmax选择策略的学习部分进行建模,我们使用一种贝叶斯推理模型,该模型根据观察到的试验结果历史来更新估计的奖励率。具有贝叶斯学习的Softmax能更好地拟合55%参与者的行为,而其他参与者则更适合不依赖学习的WSLS策略。在Softmax “使用者” 中,快感缺失程度较高的人不太可能选择估计奖励最高的选项。此外Softmax参数介导了快感缺失与总体金钱收益之间的负相关关系。另一方面,在WSLS “使用者” 中,较高的状态焦虑与WSLS相对于Softmax解释受试者逐次试验选择的能力越来越好相关。总之,在基于奖励的探索性决策中个体之间存在显著差异,并且这种差异至少部分地以一种非常特定的方式由情感属性(如享乐基调与状态焦虑)介导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e0a/5653291/9f1828f633b4/pone.0186473.g001.jpg

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