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

Author

Listed:
  • Ondrej Zika

    (Max Planck Institute for Human Development
    Max Planck UCL Centre for Computational Psychiatry and Aging Research)

  • Katja Wiech

    (University of Oxford)

  • Andrea Reinecke

    (University of Oxford
    Warneford Hospital)

  • Michael Browning

    (University of Oxford
    Warneford Hospital)

  • Nicolas W. Schuck

    (Max Planck Institute for Human Development
    Max Planck UCL Centre for Computational Psychiatry and Aging Research
    Universität Hamburg)

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.

Suggested Citation

  • Ondrej Zika & Katja Wiech & Andrea Reinecke & Michael Browning & Nicolas W. Schuck, 2023. "Trait anxiety is associated with hidden state inference during aversive reversal learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39825-3
    DOI: 10.1038/s41467-023-39825-3
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    2. Toby Wise & Raymond J. Dolan, 2020. "Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    3. G. Elliott Wimmer & Christian Büchel, 2019. "Learning of distant state predictions by the orbitofrontal cortex in humans," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    4. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
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