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Understanding risk tolerance is crucial for predicting and changing behavior across various domains, including health and safety, finance, and ethics. This remains true during a crisis, such as the COVID‐19 pandemic, and leads to a key question: Do current risk measures reliably predict risk‐taking in the drastically different context of a pandemic? The Domain Specific Risk‐Taking (DOSPERT) scale, one of the most widely used risk‐taking measures, assesses self‐reported risk‐taking in response to 30 risky situations across five domains. With the hypothetical risks of the DOSPERT being based on prepandemic circumstances, we estimate that three out of four of its risk‐taking situations were not possible due to preventive measures or did not reflect risk‐taking in times of COVID‐19. In addition, COVID‐19 brought forth new behaviors deemed risky. With an aim to better predict risk‐taking in times of a pandemic, we introduce the Pandemic DOSPERT (PDOSPERT). We summarize three preregistered online studies with 1254 UK participants to validate the scale against the original DOSPERT and three other common risk‐taking measures. We also test its ability to predict pandemic risk‐related behaviors at three points in time over 2 years. Overall, we find that the PDOSPERT scale significantly improves predictions for pandemic‐related risk behavior as compared to the original DOSPERT. In particular, the health/safety subscale is significantly and strongly associated with pandemic‐related risk behavior. We not only validate a pandemic‐specific risk task but also introduce a template for developing context‐ and domain‐sensitive measures for risk‐taking in the future. [ABSTRACT FROM AUTHOR]

Hyper‐realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super‐recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical‐recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre‐task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper‐realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed. [ABSTRACT FROM AUTHOR]