Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-re...

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Autores principales: Yamamori, Yumeya, Robinson, Oliver J, Roiser, Jonathan P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645421/
https://www.ncbi.nlm.nih.gov/pubmed/37963085
http://dx.doi.org/10.7554/eLife.87720
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author Yamamori, Yumeya
Robinson, Oliver J
Roiser, Jonathan P
author_facet Yamamori, Yumeya
Robinson, Oliver J
Roiser, Jonathan P
author_sort Yamamori, Yumeya
collection PubMed
description Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
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spelling pubmed-106454212023-11-14 Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance Yamamori, Yumeya Robinson, Oliver J Roiser, Jonathan P eLife Neuroscience Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety. eLife Sciences Publications, Ltd 2023-11-14 /pmc/articles/PMC10645421/ /pubmed/37963085 http://dx.doi.org/10.7554/eLife.87720 Text en © 2023, Yamamori et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Yamamori, Yumeya
Robinson, Oliver J
Roiser, Jonathan P
Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title_full Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title_fullStr Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title_full_unstemmed Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title_short Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
title_sort approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645421/
https://www.ncbi.nlm.nih.gov/pubmed/37963085
http://dx.doi.org/10.7554/eLife.87720
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