焦虑
无血性
习得的无助感
心理学
认知心理学
概率逻辑
压力(语言学)
发展心理学
临床心理学
计算机科学
人工智能
精神科
语言学
哲学
精神分裂症(面向对象编程)
作者
Marc Guitart-Masip,Amy Walsh,Peter Dayan,Andreas Olsson
标识
DOI:10.1038/s41598-023-45179-z
摘要
Abstract Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. Such updating is however useful only if action values are sufficiently stable, something that a lack of control may impair. We examined whether self-reported stress uncontrollability during the first wave of the COVID-19 pandemic predicted impaired reward-learning. In a preregistered study during the first-wave of the COVID-19 pandemic, we used self-reported measures of depression, anxiety, uncontrollable stress, and COVID-19 risk from 427 online participants to predict performance in a three-armed-bandit probabilistic reward learning task. As hypothesised, uncontrollable stress predicted impaired learning, and a greater proportion of probabilistic errors following negative feedback for correct choices, an effect mediated by state anxiety. A parameter from the best-fitting hidden Markov model that estimates expected beliefs that the identity of the optimal choice will shift across images, mediated effects of state anxiety on probabilistic errors and learning deficits. Our findings show that following uncontrollable stress, anxiety promotes an overly volatile representation of the reward-structure of uncertain environments, impairing reward attainment, which is a potential path to anhedonia in depression.
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