Perceptions of an individual can change dramatically across different images of their face. Questions remain as to whether some traits are more sensitive to image variability than others. To investigate this issue, we constructed a database of 340 naturalistic images consisting of 20 photos of 17 individuals. In this preregistered study, 95 participants rated all 340 images on one of three traits: trustworthiness, dominance, or attractiveness. Across images, participants’ trustworthiness ratings tended to vary more than dominance, which in turn varied more than attractiveness; however, the relative differences between traits depended on the identity in question. Importantly, despite the variability in ratings within identities, there were substantial differences between individuals, suggesting that these trait judgements are based to some degree on relatively invariant facial characteristics. We found greater between-identity variability for attractiveness judgements compared to trustworthiness and dominance. Future research should further investigate the extent to which each trait dimension is tied to the identity of the faces.
BatesD.MaechlerM.BolkerB.WalkerS. (2015).
Fitting linear mixed-effects models using lme4. Journal of Statistical Software,
67, 1–48. https://doi.org/10.18637/jss.v067.i01
BurtonA. M. (2013).
Why has research in face recognition progressed so slowly? The importance of variability. The Quarterly Journal of Experimental Psychology,
66, 1467–1485. https://doi.org/10.1068/p3335
4.
BurtonA. M.KramerR. S.RitchieK. L.JenkinsR. (2016).
Identity from variation: Representations of faces derived from multiple instances. Cognitive Science,
40, 202–223. https://doi.org/10.1111/cogs.12231
FavelleS. K.PalmisanoS.AveryG. (2011).
Face viewpoint effects about three axes: The role of configural and featural processing. Perception,
40, 761–784. https://doi.org/10.1068/p6878
HaxbyJ. V.HoffmanE. A.GobbiniM. I. (2000).
The distributed human neural system for face perception. Trends in Cognitive Sciences,
4, 223–233. https://doi.org/10.1016/S1364-6613(00)01482-0
9.
HehmanE.FlakeJ. K.FreemanJ. B. (2015).
Static and dynamic facial cues differentially affect the consistency of social evaluations. Personality and Social Psychology Bulletin,
41, 1123–1134. https://doi.org/0.1177/0146167215591495
10.
JaegerB.WagemansF. M.EvansA. M.van BeestI. (2018).
Effects of facial skin smoothness and blemishes on trait impressions. Perception,
47, 608–625. https://doi.org/10.1177/0301006618767258
Jones, B. C., DeBruine, L. M., Flake, J. K., Liuzza, M. T., Antfolk, J., Arinze, N. C., Ndukaihe, L.G., Bloxsom, N. G., Lewis, S. C., Foroni, F., Willis, M. L., Cubillas, C. P., Vadillo, M. A., Turiegano, E., Gilead, M., Simchon, A., Saribay, S. A., Owsley, N. C., Jang, C … & Coles, N. A. (2021). To which world regions does the valence -dominance model of social perception apply?. Nature Human Behaviour, 5, 159–169.
15.
JuddC. M.WestfallJ.KennyD. A. (2012).
Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology,
103, 54.
16.
KojiS.FernandesM. (2010).
Does it matter where we meet? The role of emotional context in evaluative first impressions. Canadian Journal of Experimental Psychology,
64, 107. https://doi.org/10.1037/a0019139
17.
KramerR. S.MilevaM.RitchieK. L. (2018).
Inter-rater agreement in trait judgements from faces. PLoS One,
13, e0202655.
18.
LangloisJ. H.KalakanisL.RubensteinA. J.LarsonA.HallamM.SmootM. (2000).
Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin,
126, 390–423. https://doi.org/10.1037/0033-2909.110
19.
LoS.AndrewsS. (2015).
To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology,
6, 1171. https://doi.org/10.3389/fpsyg.2015.01171
20.
MeteyardL.DaviesR. A. (2020).
Best practice guidance for linear mixed-effects models in psychological science. Journal of Memory and Language,
112, 104092.
21.
MilevaM.KramerR. S.BurtonA. M. (2019).
Social evaluation of faces across gender and familiarity. Perception,
48, 471–486.
22.
NgV. K.CribbieR. A. (2017).
Using the gamma generalized linear model for modeling continuous, skewed and heteroscedastic outcomes in psychology. Current Psychology,
36, 225–235. https://doi.org/10.1007/s12144-015-9404-0
23.
OosterhofN. N.TodorovA. (2008).
The functional basis of face evaluation. Proceedings of the National Academy of Sciences of the USA,
105, 11087–11092. https://doi.org/10.1073/pnas.0805664105
24.
PhillipsP. J.WechslerH.HuangJ.RaussP. J. (1998).
The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing,
16, 295–306. https://doi.org/10.1109/34.879790
25.
R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
RobinsE.SusiloT.RitchieK. L.DevueC. (2018). Within-person variability promotes learning of internal facial features and facilitates perceptual discrimination and memory. https://doi.org/10.31219/osf.io/5scnm
29.
SimmonsJ. P.NelsonL. D.SimonsohnU. (2012).
A 21 word solution. Dialogue: The Official Newsletter of the Society for Personality and Social Psychology,
26, 4–7. https://doi.org/10.2139/ssrn.2160588
30.
SutherlandC.OldmeadowJ.YoungA. (2014).
Are first impressions the same for male and female faces?Journal of Vision,
14, 1275–1275.
31.
SutherlandC. A.BurtonN. S.WilmerJ. B.BloklandG. A.GermineL.PalermoR.RhodesG. (2020).
Individual differences in trust evaluations are shaped mostly by environments, not genes. Proceedings of the National Academy of Sciences,
117, 10218–10224.
32.
SutherlandC. A.OldmeadowJ. A.SantosI. M.TowlerJ.BurtD. M.YoungA. W. (2013).
Social inferences from faces: Ambient images generate a three-dimensional model. Cognition,
127, 105–118. https://doi.org/10.1016/j.cognition.2012.12.001
33.
SutherlandC. A.YoungA. W.RhodesG. (2017).
Facial first impressions from another angle: How social judgements are influenced by changeable and invariant facial properties. British Journal of Psychology,
108, 397–415. https://doi.org/10.1111/bjop.12206
34.
TiddemanB.BurtM.PerrettD. (2001).
Prototyping and transforming facial textures for perception research. IEEE Computer Graphics and Applications,
21, 42–50.
35.
TodorovA.MandisodzaA. N.GorenA.HallC. C. (2005).
Inferences of competence from faces predict election outcomes. Science,
308, 1623–1626. https://doi.org/10.1126/science.1110589
36.
TodorovA.PorterJ. M. (2014).
Misleading first impressions: Different for different facial images of the same person. Psychological Science,
25, 1404–1417. https://doi.org/10.1177/0956797614532474
37.
VernonR. J.SutherlandC. A.YoungA. W.HartleyT. (2014).
Modelling first impressions from highly variable facial images. Proceedings of the National Academy of Sciences,
111, E3353–E3361. https://doi.org/10.1073/pnas.1409860111
38.
Wagenmakers, E. J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11(1), 192–196.
39.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag. https://ggplot2.tidyverse.org
WinterB. (2013).
A very basic tutorial for performing linear mixed effects analyses. arXiv Preprint,
arXiv:1308.5499. https://doi.org/10.25334/Q4W716
43.
ZebrowitzL. A.RhodesG. (2004).
Sensitivity to “bad genes” and the anomalous face overgeneralization effect: Cue validity, cue utilization, and accuracy in judging intelligence and health. Journal of Nonverbal Behavior,
28, 167–185. https://doi.org/10.1023/B:JONB.0000039648.30935.1b
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
