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Published April 2023 | Accepted Version + Supplemental Material
Journal Article Open

Computationally-defined markers of uncertainty aversion predict emotional responses during a global pandemic


Exposure to stressful life events involving threat and uncertainty often results in the development of anxiety. However, the factors that confer risk and resilience for anxiety following real world stress at a computational level remain unclear. We identified core components of uncertainty aversion moderating response to stress posed by the COVID-19 pandemic derived from computational modeling of decision making. Using both cross-sectional and longitudinal analyses, we investigated both immediate effects at the onset of the stressor, as well as medium-term changes in response to persistent stress. 479 subjects based in the United States completed a decision-making task measuring risk aversion, loss aversion, and ambiguity aversion in the early stages of the pandemic (March 2020). Self-report measures targeting threat perception, anxiety, and avoidant behavior in response to the pandemic were collected at the same time point and 8 weeks later (May 2020). Cross-sectional analyses indicated that higher risk aversion predicted higher perceived threat from the pandemic, and ambiguity aversion for guaranteed gains predicted perceived threat and pandemic-related anxiety. In longitudinal analyses, ambiguity aversion for guaranteed gains predicted greater increases in perceived infection likelihood. Together, these results suggest that individuals who have a low-level aversion toward uncertainty show stronger negative emotional reactions to both the onset and persistence of real-life stress.

Additional Information

© 2022 American Psychological Association. Received May 17, 2021. Revision received December 7, 2021. Accepted December 15, 2021. Authors: Toby Wise and Tomislav D. Zbozinek contributed equally to the article. This work was supported by Wellcome Trust Sir Henry Wellcome Fellowships to Toby Wise and Caroline J. Charpentier (206460/Z/17/Z and 218642/Z/19/Z). Toby Wise is supported by a Professor Anthony Mellows Fellowship. Tomislav D. Zbozinek is supported by the National Science Foundation (1911441). Dean Mobbs is supported by an Award from the Merkin Institute for Translational Research, US National Institute of Mental Health Grant 2P50MH094258, and a Chen Institute Award (P2026052). This research was funded, in whole or in part, by The Wellcome Trust, Grant 20640/Z/17/Z and 218642/Z/19/Z. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted article version arising from this submission. OSF repository: https://osf.io/jgpex/.

Attached Files

Accepted Version - 2022-67201-001-acc.pdf

Supplemental Material - EMO-2021-2669_Supplementary_Materials.pdf


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October 9, 2023
October 24, 2023