Trait anxiety is associated with hidden state inference during aversive reversal learning

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作者
Ondrej Zika
Katja Wiech
Andrea Reinecke
Michael Browning
Nicolas W. Schuck
机构
[1] Max Planck Institute for Human Development,Max Planck Research Group NeuroCode
[2] Max Planck UCL Centre for Computational Psychiatry and Aging Research,Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences
[3] University of Oxford,Department of Psychiatry
[4] University of Oxford,Oxford Health NHS Trust
[5] Warneford Hospital,Institute of Psychology
[6] Universität Hamburg,undefined
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Updating beliefs in changing environments can be driven by gradually adapting expectations or by relying on inferred hidden states (i.e. contexts), and changes therein. Previous work suggests that increased reliance on context could underly fear relapse phenomena that hinder clinical treatment of anxiety disorders. We test whether trait anxiety variations in a healthy population influence how much individuals rely on hidden-state inference. In a Pavlovian learning task, participants observed cues that predicted an upcoming electrical shock with repeatedly changing probability, and were asked to provide expectancy ratings on every trial. We show that trait anxiety is associated with steeper expectation switches after contingency reversals and reduced oddball learning. Furthermore, trait anxiety is related to better fit of a state inference, compared to a gradual learning, model when contingency changes are large. Our findings support previous work suggesting hidden-state inference as a mechanism behind anxiety-related to fear relapse phenomena.
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