Counteracting uncertainty: exploring the impact of anxiety on updating predictions about environmental states.

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
David Harris, Tom Arthur, Mark Wilson, Ben Le Gallais, Thomas Parsons, Ally Dill, Sam Vine
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引用次数: 0

Abstract

Anxious emotional states disrupt decision-making and control of dexterous motor actions. Computational work has shown that anxiety-induced uncertainty alters the rate at which we learn about the environment, but the subsequent impact on the predictive beliefs that drive action control remains to be understood. In the present work we tested whether anxiety alters predictive (oculo)motor control mechanisms. Thirty participants completed an experimental task that consisted of manual interception of a projectile performed in virtual reality. Participants were subjected to conditions designed to induce states of high or low anxiety using performance incentives and social-evaluative pressure. We measured subsequent effects on physiological arousal, self-reported state anxiety, and eye movements. Under high pressure conditions we observed visual sampling of the task environment characterised by higher variability and entropy of position prior to release of the projectile, consistent with an active attempt to reduce uncertainty. Computational modelling of predictive beliefs, using gaze data as inputs to a partially observable Markov decision process model, indicated that trial-to-trial updating of predictive beliefs was reduced during anxiety, suggesting that updates to priors were constrained. Additionally, state anxiety was related to a less deterministic mapping of beliefs to actions. These results support the idea that organisms may attempt to counter anxiety-related uncertainty by moving towards more familiar and certain sensorimotor patterns.

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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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