Altered learning under uncertainty in unmedicated mood and anxiety disorders


Anxiety is characterized by altered responses under uncertain conditions, but the precise mechanism by which uncertainty changes the behaviour of anxious individuals is unclear. Here we probe the computational basis of learning under uncertainty in healthy individuals and individuals suffering from a mix of mood and anxiety disorders. Participants were asked to choose between four competing slot machines with fluctuating reward and punishment outcomes during safety and stress. We predicted that anxious individuals under stress would learn faster about punishments and exhibit choices that were more affected by those punishments, thus formalizing our predictions as parameters in reinforcement learning accounts of behaviour. Overall, the data suggest that anxious individuals are quicker to update their behaviour in response to negative outcomes (increased punishment learning rates). When treating anxiety, it may therefore be more fruitful to encourage anxious individuals to integrate information over longer horizons when bad things happen, rather than try to blunt their responses to negative outcomes.

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Fig. 1: Task schematic.
Fig. 2: Group difference in parameters.
Fig. 3: Sensitivity plots.
Fig. 4: Continuous symptom analysis.

Data availability

All data used in this analysis are available on OSF at (ref. 16).

Code availability

Scripts for model fitting are available on OSF at (ref. 16) and in the Supplementary Software. For the hBayesDM package, please see


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This research was funded by a Medical Research Foundation Equipment Competition grant (no. C0497; principal investigator O.J.R.) and a Medical Research Council Career Development Award to O.J.R. (no. MR/K024280/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

O.J.R., J.A. and R.L.B. conceived and designed the study and acquired the data. O.J.R., J.A., V.V., J.P.R., P.D. and W.-Y.A. analysed and interpreted the data. W.-Y.A. and O.J.R. contributed to the creation of new software used in this work. All authors drafted the Article or substantively revised it and all authors approved the Article and are individually accountable for their own contributions.

Correspondence to Oliver J. Robinson.

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Peer review information: Primary Handling Editor: Mary Elizabeth Sutherland.

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Supplementary information

Supplementary Information

Supplementary Methods 1, Supplementary Results 1 and 2, Supplementary Tables 1−3 and Supplementary References.

Reporting Summary

Supplementary Software

A PDF version of the script used to fit the models reported in this paper. For the downloadable and editable script, as well as a link to the required hBayesDM package for R, please see the link in the Code availability statement.

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