We examined whether, even at zero acquaintance, observers accurately infer others’ social network positions—specifically, the number and patterning of social ties (e.g., brokerage—the extent to which a person bridges disconnected people) and the trait impressions that support this accuracy. We paired social network data (n = 272 professional school students), with naive observers’ (n = 301 undergraduates) judgments of facial images of each person within the network. Results revealed that observers’ judgments of targets’ number of friends were predicted by the actual number of people who considered the target a friend (in-degree centrality) and that perceived brokerage was significantly predicted by targets’ actual brokerage. Lens models revealed that targets’ perceived attractiveness, dominance, warmth, competence, and trustworthiness supported this accuracy, with attractiveness and warmth most associated with perceptions of popularity and brokerage. Overall, we demonstrate accuracy in naive observers’ judgments of social network position and the trait impressions supporting these inferences.
AmbadyN.RosenthalR. (1993). Half a minute: Predicting teacher evaluations from thin slices of nonverbal behavior and physical attractiveness. Journal of Personality and Social Psychology, 64, 431–441.
BatesD.MächlerM.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
4.
BrandsR. A. (2013). Cognitive social structures in social network research: A review. Journal of Organizational Behavior, 34, 82–103. https://doi.org/10.1002/job.1890
BrunswikE. (1955). Representative design and probabilistic theory in functional psychology. Psychological Review, 62, 193–217.
8.
BurtR. S. (1992). Structural holes: The social structure of competition. Harvard University Press.
9.
ChengJ. T.TracyJ. L.FoulshamT.KingstoneA.HenrichJ. (2013). Two ways to the top: Evidence that dominance and prestige are distinct yet viable avenues to social rank and influence. Journal of Personality and Social Psychology, 104(1), 103–125.
10.
ConnellyB. S.OnesD. S. (2010). An other perspective on personality: Meta-analytic integration of observers’ accuracy and predictive validity. Psychological Bulletin, 136(6), 1092–1122. https://doi.org/10.1037/a0021212
11.
CsardiG.NepuszT. (2006). The igraph software package for complex network research. InterJournal, Complex Systems 1695. http://igraph.org
12.
FeilerD. C.KleinbaumA. M. (2015). Popularity, similarity, and the network extraversion bias. Psychological Science, 26(5), 593–603. https://doi.org/10.1177/0956797615569580
13.
FreemanJ. B.StolierR. M.BrooksJ. A. (2020). Dynamic interactive theory as a domain-general account of social perception. In GawronskiB. (Ed.), Advances in Experimental Social Psychology (Vol. 61, pp. 237–287). Academic Press Inc. https://doi.org/10.1016/bs.aesp.2019.09.005
HallJ. A.FriedmanG. B. (1999). Status, gender and nonverbal behavior: A study of structured interactions between employees of a company. Personality and Social Psychology Bulletin, 25, 1082–1091.
16.
HehmanE.FlakeJ. K.FreemanJ. B. (2018). The faces of group members share physical resemblance. Personality and Social Psychology Bulletin, 44, 3–15.
17.
HehmanE.SutherlandC. A. M.FlakeJ. K.SlepianM. L. (2017). The unique contributions of perceiver and target characteristics in person perception. Journal of Personality and Social Psychology, 113(4), 513–529.
18.
HehmanE.XieS. Y.OfosuE. K.NespoliG. A. (2018, February19). Assessing the point at which averages are stable: A tool illustrated in the context of person perception. https://doi.org/10.31234/osf.io/2n6jq
19.
HoltzmanN. S. (2011). Facing a psychopath: Detecting the dark triad from emotionally-neutral faces, using prototypes from the Personality Faceaurus. Journal of Research in Personality, 45(6), 648–654.
20.
JanicikLarrickG. A.R. P. (2005). Social network schemas and the learning of incomplete networks. Journal of Personality and Social Psychology, 88(2), 348–364. https://doi.org/10.1037/0022-3514.88.2.348
21.
JuddC. M.WestfallJ.KennyD. A. (2017). Experiments with more than one random factor: Designs, analytic methods, and statistical power. Annual Review of Psychology, 68(17), 17.1–17.25. https://doi.org/10.1146/annurev-psych-122414-033702
22.
KennyD. A. (1994). Interpersonal perception: A social relations analysis. Guilford.
23.
KilduffM.CrosslandC.TsaiW.KrackhardtD. (2008). Organizational network perceptions versus reality: A small world after all?Organizational Behavior and Human Decision Processes, 107(1), 15–28. https://doi.org/10.1016/j.obhdp.2007.12.003
24.
KilduffM.KrackhardtD. (1994). Bringing the individual back in: A structural analysis of the internal markets for reputation in organizations. Academy of Management Journal, 37(1), 87–108. https://doi.org/10.2307/256771
25.
KovacsB.KleinbaumA. M. (2020). Language-style similarity and social networks. Psychological Science, 31(2), 202–213.
KrackhardtD. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342. https://doi.org/10.2307/2393394
28.
KuznetsovaA.BrockhoffP. B.ChristensenR. H. B. (2015). lmer-test: Tests in linear mixed effects models, R package version 2.0-30. https://CRAN.R-project.org/package=lmerTest
29.
LangnerO.DotschR.BijlstraG.WigboldusD. H. J.HawkS. T.van KnippenbergA. (2010). Presentation and validation of the Radboud faces database. Cognition & Emotion, 24(8), 1377–1388. https://doi.org/10.1080/02699930903485076
30.
MazurA.MazurJ.KeatingC. (1984). Military rank attainment of a west point class: Effects of cadets’ physical features. American Journal of Sociology, 90(1), 125–150. https://doi.org/10.1086/228050
31.
MobasseriS.SrivastavaS. B.CarneyD. R. (2016). Seeing social structure: Assessing the accuracy of interpersonal judgments about social networks (IRLE Working Paper No. 106-16). Institute for Research on Labor and Employment. http://irle.berkeley.edu/workingpapers/106-16.pdf
32.
MuthénL. K.MuthénB. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599–620. https://doi.org//10.1207/S15328007SEM0904_8
33.
OosterhofN. N.TodorovA. (2008). The functional basis of face evaluation. Proceedings of the National Academy of Sciences, 105(32), 11087–11092. https://doi.org/10.1073/pnas.0805664105
34.
ParkinsonC.KleinbaumA. M.WheatleyT. (2017). Spontaneous neural encoding of social network position. Nature Human Behavior, 1, 1–7. https://doi.org/10.1038/s41562-017-0072
35.
R Core Development Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
36.
RuleN. O.AmbadyN. (2008). The face of success: Inferences from chief executive officers’ appearance predict company profits. Psychological Science, 19(2), 109–111. https://doi.org/10.1111/j.1467-9280.2008.02054.x
37.
RuleN. O.AmbadyN. (2010). First impressions of the face: Predicting success from the face. Social and Personality Psychology Compass, 4(8), 506–516. https://doi.org/10.1111/j.1751-9004.2010.00282.x
38.
SmithE. B.BrandsR. A.BrashearsM. E.KleinbaumA. M. (2020). Social networks and cognition. Annual Review of Sociology, 46, 1–16.
39.
StewartT. R. (2001). The lens model equation. In HammondK. R.StewartT. R. (Eds.), The essential Brunswik: Beginnings, explications, applications (pp. 357–362). Oxford University Press.
40.
TuckerL. R. (1964). A suggested alternative formulation in the developments by Hursch, Hammond, and Hursch, and by Hammond, Hursch, and Todd. Psychological Review, 71, 528–530.
41.
WeisbuchM.AmbadyN. (2010). Thin slice vision. In AdamsR. B.JrAmbadyN.NakayamaK.ShimojoS. (Eds.), Social vision (pp. 28–247), Oxford University Press.
42.
YuS.KilduffG. J. (2020). Knowing where others stand: Accuracy and performance effects of individuals’ perceived status hierarchies. Journal of Personality and Social Psychology, 119(1), 159–184. https://doi.org/10.1037/pspi0000216
43.
ZhangD.GuoT.PanH.HouJ.FengZ.YangL.LinH.XiaF. (2019, May13). Judging a book by its cover: The effect of facial perception on centrality in social networks [Paper presentation]. The Web Conference (WWW), San Francisco, CA, United States.
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.
