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  Modelling avoidance in mood and anxiety disorders using reinforcement-learning

Mkrtchian, A., Aylward, J., Dayan, P., Roiser, J., & Robinson, O. (2017). Modelling avoidance in mood and anxiety disorders using reinforcement-learning. Biological Psychiatry, 82(7), 532-539. doi:10.1016/j.biopsych.2017.01.017.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-C07D-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-C07E-8
Genre: Journal Article

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Mkrtchian, A, Author
Aylward, J, Author
Dayan, P1, Author              
Roiser, JP, Author
Robinson, OJ, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Background Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior—avoiding social situations for fear of embarrassment, for instance—is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Methods Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. Results We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. Conclusions This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.

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 Dates: 2017-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.biopsych.2017.01.017
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Title: Biological Psychiatry
  Other : Biol. Psychiatry
Source Genre: Journal
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Publ. Info: New York : Elsevier
Pages: - Volume / Issue: 82 (7) Sequence Number: - Start / End Page: 532 - 539 Identifier: ISSN: 0006-3223
CoNE: https://pure.mpg.de/cone/journals/resource/954925384111