Altered learning under uncertainty in unmedicated mood and anxiety disorders
Jessica Aylward,
Vincent Valton,
Woo-Young Ahn,
Rebecca L. Bond,
Peter Dayan,
Jonathan P. Roiser and
Oliver J. Robinson ()
Additional contact information
Jessica Aylward: University College London
Vincent Valton: University College London
Woo-Young Ahn: Seoul National University
Rebecca L. Bond: University College London
Peter Dayan: University College London
Jonathan P. Roiser: University College London
Oliver J. Robinson: University College London
Nature Human Behaviour, 2019, vol. 3, issue 10, 1116-1123
Abstract:
Abstract 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.
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:nat:nathum:v:3:y:2019:i:10:d:10.1038_s41562-019-0628-0
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DOI: 10.1038/s41562-019-0628-0
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