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The computational structure of consummatory anhedonia

Hall, Anna F; Browning, Michae; Huys, Quentin JM; (2024) The computational structure of consummatory anhedonia. Trends in Cognitive Sciences 10.1016/j.tics.2024.01.006. (In press). Green open access

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Abstract

Anhedonia is a reduction in enjoyment, motivation, or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed ‘consummatory anhedonia’, in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement learning (RL) account of consummatory anhedonia with a reconceptualisation of subjective hedonic experience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.

Type: Article
Title: The computational structure of consummatory anhedonia
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.tics.2024.01.006
Publisher version: http://dx.doi.org/10.1016/j.tics.2024.01.006
Language: English
Additional information: Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Anhedonia; reinforcement learning; reward; goals; stress response; emotion appraisal
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10189853
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