Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise.
Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise.
复制标题
焦虑中的计算功能障碍：无法区分信号与噪声。
DOI：
10.1016/j.biopsych.2017.07.007
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发表时间：
2017-09-15
影响因子：
10.6
通讯作者：
Paulus MP
中科院分区：
文献类型：
--
作者：
Huang H;Thompson W;Paulus MP
区分一个行为是偶然导致结果，还是通过表明环境变化的潜在统计规律性导致结果，会深刻影响适应行为。先前的研究表明，焦虑的人可能无法正确区分这些情况。本研究旨在精确量化焦虑个体的过程缺陷，并确定这些过程功能障碍与焦虑的程度。作为正在进行的大型临床人群研究的一部分招募的122名受试者完成了变点检测任务。强化学习模型用于解释低焦虑（OASIS<=8）和高焦虑（OASIS>=9）组中观察到的行为差异。高焦虑个体使用次优决策策略，其特征在于更高的损失转移率。计算模型和模拟显示，这种差异是由于较高的基础学习率。这些发现在上下文相关的强化学习模型中得到了更好的解释。焦虑症受试者对不确定性的夸大反应导致了一种次优的决策策略，这使得这些人难以确定一个行动是否与偶然或某种统计规律有关。这些发现对利用学习模型开发新的行为干预策略具有重要意义。
Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Prior studies have shown that anxious individuals may not appropriately differentiate between these situations. This investigation aims to precisely quantify the process deficit in anxious individuals and determine the degree to which these process dysfunctions are specific to anxiety. 122 subjects recruited as part of an ongoing large clinical population study completed a change point detection task. Reinforcement learning models were used to explicate observed behavioral differences in Low Anxiety (OASIS<=8) and High Anxiety (OASIS>=9) groups. High anxious individuals used a sub-optimal decision strategy characterized by a higher lose-shift rate. Computational models and simulations revealed that this difference was due to a higher base learning rate. These findings are better explained in a context-dependent reinforcement learning model. Anxious subjects’ exaggerated response to uncertainty leads to a suboptimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. These findings have important implications for developing new behavioral intervention strategies utilizing learning models.
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