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Anxious individuals have difficulty learning the causal statistics of aversive environments

Nature Neuroscience volume 18, pages 590596 (2015) | Download Citation



Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions. Environments differ in the extent to which action-outcome contingencies are stable or volatile. Difficulty in being able to use this information to optimally update outcome predictions might contribute to the decision-making difficulties seen in anxiety. We tested this using an aversive learning task manipulating environmental volatility. Human participants low in trait anxiety matched updating of their outcome predictions to the volatility of the current environment, as predicted by a Bayesian model. Individuals with high trait anxiety showed less ability to adjust updating of outcome expectancies between stable and volatile environments. This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.

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The authors would like to thank S. Hicks and C. Kennard for use of the eye-tracking system. This research was supported by European Research Council grant GA 260932 and US National Institutes of Health grant R01MH091848.

Author information


  1. Functional MRI of the Brain Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK.

    • Michael Browning
    • , Timothy E Behrens
    • , Gerhard Jocham
    • , Jill X O'Reilly
    •  & Sonia J Bishop
  2. Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany.

    • Gerhard Jocham
  3. Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.

    • Sonia J Bishop


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M.B. and S.J.B. wrote the manuscript. M.B., S.J.B. and T.E.B. designed the task. M.B. collected the data. All of the authors contributed to data analysis. T.E.B. developed the Bayesian model. All of the authors commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Sonia J Bishop.

Integrated supplementary information

Supplementary figures

  1. 1.

    Calibration of Electrical Shocks.

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    Effects of volatility upon participants’ learning rates in a structurally equivalent reward task.

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    Influence of potential shock magnitude and prior outcome history on actual and simulated behavior choice as a function of block volatility (a,b) and participant anxiety (c-f).

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    Relationship between trait anxiety and Bayesian volatility when multiple parameters, including a decay function, are allowed to compete for influence over a dynamic learning rate.

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    Shock receipt on difficult trials (where the two options were close in expected value) was positively correlated with trait anxiety (a), and negatively correlated with both change in learning rate between stable and volatile blocks (b) and modulation of post-outcome pupil dilation by trial volatility (c).

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    No significant association was observed between Trait Anxiety and Risk Preference or Inverse Decision Temperature.

  7. 7.

    Analyses of reaction time and pupil dilation data using non-Bayesian estimates of trial volatility and outcome surprise

  8. 8.

    Baseline corrected, z-transformed eyetracker traces from the aversive learning task.

  9. 9.

    The relationship between trait anxiety and post outcome pupil dilation as a function of trial-wise estimates of volatility, controlling for trial number within each block.

  10. 10.

    Results from additional pupil analyses investigating the interaction between outcome (shock received versus no shock received), and volatility (a) and surprise (b).

  11. 11.

    Interaction of estimated trialwise volatility and surprise on pupil dilation post outcome.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Modeling Note

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