Updating beliefs under perceived threat
Open accessAbstract
Humans are better at integrating desirable information into their beliefs than undesirable information. This asymmetry poses an evolutionary puzzle, as it can lead to an underestimation of risk and thus failure to take precautionary action. Here, we suggest a mechanism that can speak to this conundrum. In particular, we show that the bias vanishes in response to perceived threat in the environment. We report that an improvement in participants9 tendency to incorporate bad news into their beliefs is associated with physiological arousal in response to threat indexed by galvanic skin response and self-reported anxiety. This pattern of results was observed in a controlled laboratory setting (Experiment I), where perceived threat was manipulated, and in firefighters on duty (Experiment II), where it naturally varied. Such flexibility in how individuals integrate information may enhance the likelihood of responding to warnings with caution in environments rife with threat, while maintaining a positivity bias otherwise, a strategy that can increase well-being. SIGNIFICANCE STATEMENT The human tendency to be overly optimistic has mystified scholars and lay people for decades: How could biased beliefs have been selected over unbiased beliefs? Scholars have suggested that although the optimism bias can lead to negative outcomes, including financial collapse and war, it can also facilitate health and productivity. Here, we demonstrate that a mechanism generating the optimism bias, namely asymmetric information integration, evaporates under threat. Such flexibility could result in enhanced caution in dangerous environments while supporting an optimism bias otherwise, potentially increasing well-being.
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The optimism bias disappears when subjects perceive a threatening environment.
“This mech anism, also known as optimism bias, disappears when subjects perceive a threatening environment (Garrett et al., 2018).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Garrett et al., 2018
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- Authors
- Garrett, N., González-Garzón, A. M., Foulkes, L., Levita, L., & Sharot, T.
- Affiliations
- Princeton University (US)
- University College London (GB)
- University of Sheffield (GB)
- Corresponding author
- Garrett, Neil
- Sharot, Tali
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2018-08-06
- Volume
- 38
- Issue
- 36
- Pages
- 7901-7911
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6125815
- OpenAlex
- W2885599406
- Cited by
- 109
- References
- 58
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 13 · 2023: 14 · 2022: 17 · 2021: 12 · 2020: 17 · 2019: 6
- FWCI
- 7.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Neuroscience (publishedVersion, cc-by)
- SSRN Electronic Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Applied Psychology › Optimism, Hope, and Well-being
- Keywords
- optimism bias, positivity bias, threat perception, risk underestimation, skin conductance, physiological arousal, anxiety, firefighters, belief updating, precautionary behavior
- MeSH terms
- Optimism, Adult, Anxiety, Arousal, Female, Galvanic Skin Response, Humans, Male, Stress, Psychological, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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