2022
DOI: 10.1521/soco.2022.40.2.127
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Who Can Be Fooled? Modeling Facial Impressions of Gullibility

Abstract: The success of acts of deceit and exploitation depends on how trusting and naïve (i.e., gullible) targets are. In three preregistered studies, using both theory-driven and data-driven approaches, we examined how people form impressions of gullibility based on targets' facial appearance. We find significant consensus in gullibility impressions, suggesting that people have a somewhat shared representation of what a gullible person looks like (Study 1, n = 294). Gullibility impressions is based on different cues … Show more

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Cited by 3 publications
(3 citation statements)
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“…To further investigate the role of different classes of colour characteristics in predicting the preference of real human faces, and identify the most important colour predictors, techniques from machine learning were implemented in the modelling process. Similar approaches can be found in previous studies 38 , 39 .…”
Section: Methodssupporting
confidence: 74%
See 1 more Smart Citation
“…To further investigate the role of different classes of colour characteristics in predicting the preference of real human faces, and identify the most important colour predictors, techniques from machine learning were implemented in the modelling process. Similar approaches can be found in previous studies 38 , 39 .…”
Section: Methodssupporting
confidence: 74%
“…To further investigate the role of the three different classes of colour characteristics (average/local skin colour, skin colour variation, and facial colour contrast) in predicting the preference of real human faces, and identify their relative importance, techniques from machine learning were implemented, following previous studies 38 , 39 . We used cross-validated linear regression models (fivefold cross validation with 50 repeats) to compare the predictive power of the three different classes of facial colour characteristics.…”
Section: Resultsmentioning
confidence: 99%
“…Elastic net regression (EN) combines shrinkage from RR and LASSO and balances the two algorithms by weighting the two effects [ 41 , 42 ]. The implementation of this approach in facial impression research can be found in previous work [ 43 , 44 ]. The current study assessed all the strategies mentioned above in terms of their effectiveness in attractiveness modelling from colour traits.…”
Section: Introductionmentioning
confidence: 99%