The Ability to Detect the Characteristics of the Dark Triad from Facial Composites of the Opposite Sex
Journal Title: European Journal of Behavioral Sciences - Year 2023, Vol 6, Issue 2
Abstract
Individuals often resort to a reliance on perceptions of the facial features of others as facial information is more readily accessible than stable expressions of behaviour. The ability to identify the traits of an individual from their facial features has its basis in evolution. This has been demonstrated in several studies that have focused on the detection of extroversion, friendliness, honesty, and other desirable personality traits. However, the question remains as to whether individuals also possess the ability to detect negative traits, particularly those that pose a risk to a partnership. The present study aims to investigate how accurately individuals are able to detect dark triad traits, specifically narcissism, Machiavellianism, and subclinical psychopathy, and determine if there are any differences in their assessment of the individual traits. Additionally, the study aims to investigate whether there are gender differences in the ability to detect these traits in the faces of the opposite sex, and whether the probability of the occurrence of such an ability is greater than mere chance. The research was conducted using a sample of 631 participants, 51.2% of whom were female, aged 13-80 (mean age: 36.97 years). The research used computer-generated facial composites to produce male and female faces which were either high or low in the dark triad traits. The results indicated that individuals, both male and female, were able to accurately identify the faces, to a statistically significant degree, that exhibited dark triad traits. Women achieved their best results in the detection of subclinical psychopathy in male facial composites, while men achieved their best results in the detection of Machiavellianism in female facial composites.
Authors and Affiliations
Slavka Demuthova, Andrej Demuth
Keywords
Related Articles
- EP ID EP743768
- DOI https://doi.org/10.33422/ejbs.v6i2.1055
- Views 39
- Downloads 0
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