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Encoding fear intensity in human sweat
- Source :
- Philosophical Transactions of the Royal Society B: Biological Sciences vol.375 (2020) date: 2020-06-07 nr.1800 p.1-10 [ISSN 0800-4622]
- Publication Year :
- 2020
-
Abstract
- Humans, like other animals, have an excellent sense of smell that can serve social communication. Although ample research has shown that body odours can convey transient emotions like fear, these studies have exclusively treated emotions as categorical, neglecting the question whether emotion quantity can be expressed chemically. Using a unique combination of methods and techniques, we explored a dose-response function: Can experienced fear intensity be encoded in fear sweat? Specifically, fear experience was quantified using multivariate pattern classification (combining physiological data and subjective feelings with partial least squares-discriminant analysis), whereas a photo-ionization detector (PID) quantified volatile molecules in sweat. Thirty-six male participants donated sweat while watching scary film clips and control (calming) film clips. Both traditional univariate and novel multivariate analysis (100% classification accuracy; Q2 : 0.76; R2 : 0.79) underlined effective fear induction. Using their regressionweighted scores, participants were assigned significantly above chance (83% > 33%) to fear intensity categories (low-medium-high). Notably, the high fear group (n = 12) produced higher doses of armpit sweat, and greater doses of fear sweat emitted more volatile molecules (n = 3). This study brings new evidence to show that fear intensity is encoded in sweat (dose-response function), opening a field that examines intensity coding and decoding of other chemically communicable states/traits.
Details
- Database :
- OAIster
- Journal :
- Philosophical Transactions of the Royal Society B: Biological Sciences vol.375 (2020) date: 2020-06-07 nr.1800 p.1-10 [ISSN 0800-4622]
- Notes :
- DOI: 10.1098/rstb.2019.0271, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1445810573
- Document Type :
- Electronic Resource