State anxiety biases estimates of uncertainty during reward learning in volatile environments
State anxiety biases estimates of uncertainty during reward learning in volatile environments
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DOI：
10.1101/809749
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Thomas P. Hein;Lilian A. E. Weber;J. D. de Fockert;M. H. Ruiz
中科院分区：
文献类型：
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作者：
Thomas P. Hein;Lilian A. E. Weber;J. D. de Fockert;M. H. Ruiz
先前的研究表明，临床焦虑会损害决策，高特质焦虑会干扰学习速度。暂时的焦虑状态对健康人群的学习和决策的影响还不太清楚。在这里，我们遵循的建议，健康人的焦虑状态引起异常的行为，神经和生理反应的模式与焦虑症中发现的那些，特别是在不稳定的环境中处理不确定性。在我们的研究中，状态焦虑组和对照组都在不稳定的任务环境中学习了概率刺激-结果映射，同时我们记录了他们的电生理（EEG）信号。通过使用层次贝叶斯模型，我们评估了状态焦虑对贝叶斯信念更新的影响，重点是不确定性估计。状态焦虑与环境和信息不确定性的低估以及波动性估计的不确定性增加有关。焦虑的个体认为他们对奖励偶然性的信念更精确，需要更少的更新，最终导致受损的奖励为基础的学习。我们解释这种模式的证据表明，国家焦虑的个人是不太宽容的信息不确定性的突发事件管理他们的环境和更不确定的水平，稳定的世界本身。此外，我们跟踪的信念更新信号的神经表示的试验由试验EEG振幅。在控制参与者中，较低水平的精确加权预测误差（RIPPE）的奖励结果和较高水平的波动性RIPPE的ERP信号中表示与前分布。在状态焦虑下出现了不同的模式，其中仅在有关波动性的更新中发现pwPE的神经表示。扩展以前的特质焦虑的计算工作，我们的研究结果表明，暂时的焦虑状态在健康个体损害奖励为基础的学习在不稳定的环境中，主要是通过不确定性估计的变化和潜在的退化的神经元表示的层次相关的神经元，被认为是发挥核心作用，在当前贝叶斯帐户的感知推理和学习。
Previous research established that clinical anxiety impairs decision making and that high trait anxiety interferes with learning rates. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations. Here we follow proposals that anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders, particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental and informational uncertainty, and an increase in uncertainty about volatility estimates. Anxious individuals deemed their beliefs about reward contingencies to be more precise and to require less updating, ultimately leading to impaired reward-based learning. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, both lower-level precision-weighted prediction errors (pwPEs) about the reward outcomes and higher-level volatility-pwPEs were represented in the ERP signals with an anterior distribution. A different pattern emerged under state anxiety, where a neural representation of pwPEs was only found for updates about volatility. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates and potentially a degradation of the neuronal representation of hierarchically-related pwPEs, considered to play a central role in current Bayesian accounts of perceptual inference and learning.