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

Ondrej Zika (), Katja Wiech, Andrea Reinecke, Michael Browning and Nicolas W. Schuck ()
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Ondrej Zika: Max Planck Institute for Human Development
Katja Wiech: University of Oxford
Andrea Reinecke: University of Oxford
Michael Browning: University of Oxford
Nicolas W. Schuck: Max Planck Institute for Human Development

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-39825-3

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