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Increased and biased deliberation in social anxiety

Nature Human Behaviour volume 6, pages 146–154 (2022)Cite this article

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Abstract

A goal of computational psychiatry is to ground symptoms in basic mechanisms. Theory suggests that avoidance in anxiety disorders may reflect dysregulated mental simulation, a process for evaluating candidate actions. If so, these covert processes should have observable consequences: choices reflecting increased and biased deliberation. In two online general population samples, we examined how self-report symptoms of social anxiety disorder predict choices in a socially framed reinforcement learning task, the patent race, in which the pattern of choices reflects the content of deliberation. Using a computational model to assess learning strategy, we found that self-report social anxiety was indeed associated with increased deliberative evaluation. This effect was stronger for a particular subset of feedback (‘upward counterfactual’) in one of the experiments, broadly matching the biased content of rumination in social anxiety disorder, and robust to controlling for other psychiatric symptoms. These results suggest a grounding of symptoms of social anxiety disorder in more basic neuro-computational mechanisms.

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Fig. 1: Schematic of patent race game.
Fig. 2: Distributions of scores.
Fig. 3: Change in EWA parameters as a function of LSAS and abbreviated nine-item Raven’s matrices.
Fig. 4: Change in EWA parameters as a function of LSAS and abbreviated nine-item Raven’s matrices.
Fig. 5: Per cent change in EWA parameters as a function of psychiatric symptom dimensions and Raven’s (abbreviated nine-item Raven’s matrices) for experiment 2 subjects (N = 331).

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Data availability

Processed data (per-participant estimated model parameters and covariates) supporting all of the statistical results of the study, and the raw choice data from which the model parameters were estimated, are available at https://github.com/ndawlab/patentrace. Raw psychometric data (questionnaire responses) are available from the corresponding authors upon request.

Code availability

Custom MATLAB code to reproduce all statistical results and tables is available at https://github.com/ndawlab/patentrace. Custom Julia code for estimating learning model parameters from raw choice data is available at https://github.com/ndawlab/em. Additional code (for figures and analyses of psychometric data) is available from the corresponding authors upon request.

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Acknowledgements

The authors thank Q. Huys, M. Paulus, M. Stein, A. Solway and S. Zorowitz for helpful conversations. This work was supported by NIMH grant R01MH121093, part of the CRNCS programme, and by a Scholar Award from the James S. McDonnell Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Elana A. Meer

    Present address: Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Authors and Affiliations

  1. Department of Psychology, Princeton University, Princeton, NJ, USA

    Lindsay E. Hunter & Nathaniel D. Daw

  2. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Elana A. Meer & Nathaniel D. Daw

  3. School of Psychology, Trinity College Dublin, Dublin, Ireland

    Claire M. Gillan

  4. Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

    Claire M. Gillan

  5. Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

    Claire M. Gillan

  6. Haas School of Business, University of California, Berkeley, Berkeley, CA, USA

    Ming Hsu

  7. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Ming Hsu

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Contributions

L.E.H., E.A.M., C.M.G., M.H. and N.D.D. contributed to the conception and design of the experiment L.E.H. and E.A.M. collected the data. L.E.H., E.A.M. and N.D.D. analysed the data. L.E.H. and N.D.D. prepared the initial draft of the manuscript, and all authors edited the manuscript and gave final approval of revisions.

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Correspondence to Lindsay E. Hunter or Nathaniel D. Daw.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Camilla Nord and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Tables 1–10 and Figs. 1–5.

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Hunter, L.E., Meer, E.A., Gillan, C.M. et al. Increased and biased deliberation in social anxiety. Nat Hum Behav 6, 146–154 (2022). https://doi.org/10.1038/s41562-021-01180-y

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