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Identification of acutely sick people and facial cues of sickness
Stockholm University, Faculty of Social Sciences, Stress Research Institute. Karolinska Institutet, Sweden.
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2018 (English)In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 285, no 1870, article id 20172430Article in journal (Refereed) Published
Abstract [en]

Detection and avoidance of sick individuals have been proposed as essential components in a behavioural defence against disease, limiting the risk of contamination. However, almost no knowledge exists on whether humans can detect sick individuals, and if so by what cues. Here, we demonstrate that untrained people can identify sick individuals above chance level by looking at facial photos taken 2 h after injection with a bacterial stimulus inducing an immune response (2.0 ng kg-1 lipopolysaccharide) or placebo, the global sensitivity index being d' = 0.405. Signal detection analysis (receiver operating characteristic curve area) showed an area of 0.62 (95% confidence intervals 0.60-0.63). Acutely sick people were rated by naive observers as having paler lips and skin, a more swollen face, droopier corners of the mouth, more hanging eyelids, redder eyes, and less glossy and patchy skin, as well as appearing more tired. Our findings suggest that facial cues associated with the skin, mouth and eyes can aid in the detection of acutely sick and potentially contagious people.

Place, publisher, year, edition, pages
2018. Vol. 285, no 1870, article id 20172430
Keywords [en]
social perception, facial cues, health, disease avoidance
National Category
Neurosciences Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:su:diva-151296DOI: 10.1098/rspb.2017.2430ISI: 000419973000016PubMedID: 29298938OAI: oai:DiVA.org:su-151296DiVA, id: diva2:1172540
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-02-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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  • nn-NB
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Output format
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