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Receivers Limit the Prevalence of Deception in Humans: Evidence from Diving Behaviour in Soccer Players

Overview of attention for article published in PLOS ONE, October 2011
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Title
Receivers Limit the Prevalence of Deception in Humans: Evidence from Diving Behaviour in Soccer Players
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0026017
Pubmed ID
Authors

Gwendolyn K. David, Catriona H. Condon, Candice L. Bywater, Daniel Ortiz-Barrientos, Robbie S. Wilson

Abstract

Deception remains a hotly debated topic in evolutionary and behavioural research. Our understanding of what impedes or facilitates the use and detection of deceptive signals in humans is still largely limited to studies of verbal deception under laboratory conditions. Recent theoretical models of non-human behaviour have suggested that the potential outcome for deceivers and the ability of receivers to discriminate signals can effectively maintain their honesty. In this paper, we empirically test these predictions in a real-world case of human deception, simulation in soccer. In support of theoretical predictions in signalling theory, we show that cost-free deceit by soccer players decreases as the potential outcome for the signaller becomes more costly. We further show that the ability of receivers (referees) to detect deceptive signals may limit the prevalence of deception by soccer players. Our study provides empirical support to recent theoretical models in signalling theory, and identifies conditions that may facilitate human deception and hinder its detection.

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Geographical breakdown

Country Count As %
Portugal 1 2%
Brazil 1 2%
United Kingdom 1 2%
Romania 1 2%
United States 1 2%
Unknown 40 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Researcher 6 13%
Student > Doctoral Student 5 11%
Lecturer 3 7%
Student > Bachelor 3 7%
Other 10 22%
Unknown 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 31%
Sports and Recreations 5 11%
Engineering 3 7%
Chemistry 3 7%
Computer Science 2 4%
Other 9 20%
Unknown 9 20%