@article{yamamori_approach-avoidance_2023,
	title = {Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance},
	volume = {12},
	issn = {2050-084X},
	url = {https://elifesciences.org/articles/87720},
	doi = {10.7554/eLife.87720},
	abstract = {Although avoidance is a prevalent feature of anxiety-r­elated psychopathology, differences in the measurement of avoidance between humans and non-h­ uman 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-a­ voidance reinforcement learning task, by adapting a paradigm from the non-h­ uman animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-a­ voidance 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-s­ ample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-a­ voidance 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.},
	language = {en},
	urldate = {2023-12-21},
	journal = {eLife},
	author = {Yamamori, Yumeya and Robinson, Oliver J and Roiser, Jonathan P},
	month = nov,
	year = {2023},
	pages = {RP87720},
}

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To address this, we developed a novel translational measure of anxiety-r­elated avoidance in the form of an approach-a­ voidance reinforcement learning task, by adapting a paradigm from the non-h­ uman animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-a­ voidance 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-s­ ample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-a­ voidance 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.","language":"en","urldate":"2023-12-21","journal":"eLife","author":[{"propositions":[],"lastnames":["Yamamori"],"firstnames":["Yumeya"],"suffixes":[]},{"propositions":[],"lastnames":["Robinson"],"firstnames":["Oliver","J"],"suffixes":[]},{"propositions":[],"lastnames":["Roiser"],"firstnames":["Jonathan","P"],"suffixes":[]}],"month":"November","year":"2023","pages":"RP87720","bibtex":"@article{yamamori_approach-avoidance_2023,\n\ttitle = {Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance},\n\tvolume = {12},\n\tissn = {2050-084X},\n\turl = {https://elifesciences.org/articles/87720},\n\tdoi = {10.7554/eLife.87720},\n\tabstract = {Although avoidance is a prevalent feature of anxiety-r­elated psychopathology, differences in the measurement of avoidance between humans and non-h­ uman 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-a­ voidance reinforcement learning task, by adapting a paradigm from the non-h­ uman animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-a­ voidance 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-s­ ample retested 1 week later (n = 57). 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