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Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

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  • Toby Wise

    (University College London
    University College London
    California Institute of Technology)

  • Raymond J. Dolan

    (University College London
    University College London)

Abstract

Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17977-w
    DOI: 10.1038/s41467-020-17977-w
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    Cited by:

    1. 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.

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