Although certain characteristics of human faces are broadly considered more attractive (e.g., symmetry, averageness), people also routinely disagree with each other on the relative attractiveness of faces. That is, to some significant degree, beauty is in the “eye of the beholder.” Here, we investigate the origins of these individual differences in face preferences using a twin design, allowing us to estimate the relative contributions of genetic and environmental variation to individual face attractiveness judgments or face preferences. We first show that individual face preferences (IP) can be reliably measured and are readily dissociable from other types of attractiveness judgments (e.g., judgments of scenes, objects). Next, we show that individual face preferences result primarily from environments that are unique to each individual. This is in striking contrast to individual differences in face identity recognition, which result primarily from variations in genes []. We thus complete an etiological double dissociation between two core domains of social perception (judgments of identity versus attractiveness) within the same visual stimulus (the face). At the same time, we provide an example, rare in behavioral genetics, of a reliably and objectively measured behavioral characteristic where variations are shaped mostly by the environment. The large impact of experience on individual face preferences provides a novel window into the evolution and architecture of the social brain, while lending new empirical support to the long-standing claim that environments shape individual notions of what is attractive.

Results and Discussion

15 Blokland G.A.M.

Mosing M.A.

Verweij K.H.

Medland S.E. Twin studies and behavior genetics. Figure 2 Genetic and Environmental Contributions to Individual Face Preferences and Face Recognition Show full caption (A) Shown are face IP scores for monozygotic (MZ) twin pairs and dizygotic (DZ) twin pairs. Scores from one twin are plotted on the x axis, with scores from that person’s co-twin on the y axis. Maximum likelihood correlations are shown. MZ correlations represent the combined impact of shared genetic variation and shared environments (family resemblance), whereas the difference between MZ and DZ correlations can be used to estimate the contribution of genetic variation, specifically. (B) Maximum likelihood model fitting was applied to MZ and DZ twin data to estimate A (additive genetic), C (shared environmental), and E (unshared environmental) contributions to face IP scores. Error bars give 95% CIs around each estimate (bounded by zero on the lower end). (C) The best-fit model included contributions from both additive genetic (A) and individual or unshared environmental (E) factors. Estimates of genetic and environmental contributions to both face recognition and face IP scores (face preferences) are shown, with error bars indicating 95% CIs around each estimate. Arrows indicate the upper boundary for “A” estimates, based on the test-retest reliability of each measure. Based on AE model estimates, (1) almost all of the reliable variation in face recognition is due to variations in genes, and (2) most of the reliable variation in face IP is attributable to variations in environments. This dissociation in heritability suggests that there are distinct genetic and etiological mechanisms underlying these two core social-perceptual phenotypes. See also Figure S1 and Table S2 Table 1 Reliability, Twin Correlations, and Variance Component Estimates for Face IP Reliability Internal (split-half) 0.88 Test-retest 0.75 Twin Correlations (95% CI) MZ 0.22 (0.14–0.29) DZ 0.09 (−0.06–0.24) Model Fit: −2LL; AIC (p Value) ACE 12,711.44; 9,695.44 AE 12,711.44; 9,693.44 (p = 1) CE 12,713.54; 9,695.54 (p = 0.15) E 12,741.39; 9,721.39 (p < 0.001) Full Model: ACE Estimates (95% CI) A 0.22 (0–0.29) C 0 (0–0.24) E 0.78 (0.71–0.86) Best-Fit Model: AE Estimates (95% CI) A 0.22 (0.14–0.29) E 0.78 (0.71–0.86) 16 Boker S.M.

Neale M.C.

Maes H.H.

Wilde M.J.

Spiegel M.

Brick T.R.

Spies J.

Estabrook R.

Kenny S.

Bates T.C.

Mehta P.

Fox J. OpenMx: an open source extended structural equation modeling framework. Internal or split-half reliability was estimated based on Spearman-Brown corrected correlations between face IP scores calculated from ratings on odd- versus even-numbered trials. Test-retest reliability was calculated in a separate sample based on alternate forms test-retest (see Supplemental Information : Reliability Analysis). Maximum likelihood correlations are shown for monozygotic (MZ) and dizygotic (DZ) twin pairs. Model fit parameters are given for the full ACE model, estimated using OpenMx software []. Parameter estimates represent the contribution of additive genetic (A), shared environmental (C), and individual or unshared environmental (E) factors. The AE model was selected as the best-fit model, based on Akaike’s information criterion. Parameter estimates for the reduced AE model are also shown. Parameter estimates for all models are given in Table S2 Next, we estimated the contributions of genetic and environmental factors to face IP by comparing the correlation of face IP scores among MZ twins with the correlation of face IP scores among DZ twins. Although MZ and DZ twins share family environment to a similar extent, MZ twins share, on average, twice as much of their genetic variation as DZ twins. The correlations for face IP scores between MZ twins and between DZ twins can thus be used to estimate the proportion of variation in face IP that can be explained by variations in genes, shared environments, and unshared environments. We calculated a maximum likelihood correlation of 0.22 (95% CI: 0.14–0.29) for MZ twins and 0.09 (95% CI: −0.06–0.24) for DZ twins. These two correlations did not significantly differ (Fisher r-to-z transformation; p = 0.1), indicating that most of the variance in face IP is likely attributable to environmental factors. To obtain a more precise estimate of the contributions of genetic and environmental factors to face IP, we fit a standard ACE twin model that includes additive genetic influences (A), shared environmental influences (C), as well as unshared environmental influences and measurement error (E) using structural equation modeling techniques []. Controlling for age and sex, the ACE model attributed 22% of variance to (A) additive genetic factors, 0% to (C) shared environmental factors, and 78% to (E) individual or unshared environment and/or measurement error (see Figure 2 B and Table 1 ). We compared the full ACE model with reduced AE, CE, and E models (respectively setting the contributions of the C parameter, A parameter, and A + C parameters to zero) and determined the AE model yielded the best fit based on Akaike’s information criterion (see Table 1 and Table S2 ). The AE model gave similar point estimates for both A (22%) and E (78%) parameters, but with tighter confidence intervals (see Figure 2 C and Table 1 ). We conclude that most of the reliable variation in face IP was explained by the influence of unshared or individual environment with a relatively small contribution from genetic variation and little to no contribution from shared environment.

17 Ebstein R.P.

Israel S.

Chew S.H.

Zhong S.

Knafo A. Genetics of human social behavior. 18 Cesarini D.

Dawes C.T.

Fowler J.H.

Johannesson M.

Lichtenstein P.

Wallace B. Heritability of cooperative behavior in the trust game. 19 Cesarini D.

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Wallace B. Experimental game theory and behavior genetics. 20 Kendler K.S.

Prescott C.A. Genes, Environment, and Psychopathology. 21 Conway B.R.

Rehding A. Neuroaesthetics and the trouble with beauty. 22 Little A.C.

Perrett D.I. Putting beauty back in the eye of the beholder. Our results provide a rare example of a complex, objectively measured, highly reliable, and specific behavioral characteristic that is shaped predominantly by environmental factors. Although high estimates of unshared environment contributions to social cognition and behavior are reported in twin studies [], these estimates often occur in the context of low or unknown reliability []. High measurement error (low reliability) spuriously reduces estimates of familial resemblance from both genetic and shared environmental factors and spuriously inflates estimates of unshared environmental contributions. Apparent examples of high unshared environment contributions are often confounded with measurement error []. Given the high reliability of face IP, even when estimated conservatively (via an alternate forms test-retest procedure), we conclude that the contribution of unshared environmental factors to face IP cannot be explained by unreliable measurement. Instead, our findings support the notion that individual aesthetic face preferences are truly shaped primarily by individual life experiences [].

23 Iaria G.

Fox C.J.

Waite C.T.

Aharon I.

Barton J.J. The contribution of the fusiform gyrus and superior temporal sulcus in processing facial attractiveness: neuropsychological and neuroimaging evidence. 23 Iaria G.

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Duchaine B. Human face recognition ability is specific and highly heritable. 26 Zhu Q.

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Duchaine B. Human face recognition ability is specific and highly heritable. 27 Plomin R.

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Neiderhiser J.M. Behavioral Genetics: A Primer. The observed results isolate a highly specific environmental influence that impacts face IP independently of scene IP and abstract object IP. But does this environmental influence act specifically on face attractiveness judgments? Alternatively, it might act broadly on any judgment that involves a face or on any social judgment. As a strong test of specificity, we consider the case of face identity recognition. Face attractiveness judgments and face identity recognition both involve social evaluation of faces, in the visual domain. Moreover, both require processing of invariant face characteristics, which are known to rely upon inferior occipital and inferior temporal brain regions [], and deficits in both have been found to coexist in patients []. If the etiology of face identity processing were to differ from that of face IP, then that would provide strong evidence that the observed environmental effect is specific not only to social stimuli in general, or to faces in particular, or even to judgments of invariant face characteristics, but rather to a particular subset of judgments of invariant face characteristics. We previously measured face recognition in another sample of MZ and DZ twins drawn from the same Australian Twin Registry [] (see also []). While highly reliable, the face recognition measure was no more reliable than our face IP measure (Cambridge Face Memory Test scores: internal reliability = 0.89, test-retest reliability = 0.70; face IP scores: internal reliability = 0.88, test-retest reliability = 0.75). Yet despite equal precision of measurement, a sample drawn from the same population, and similarly robust evidence for independence from various non-face categories, we found little to no impact of environment on face recognition ability. Genetic variation accounted for most or all of the reliable face recognition variance, in contrast with face IP (68% versus 22% heritability; p of difference < 1E−14; see Figure 2 C and Table 1 ). Indeed, looking across the behavioral genetic literature, face IP is among the most environmental objectively measured behavioral traits, whereas face identity recognition is among the most heritable []. We conclude from this etiological dissociation that the observed environmental effect is highly specific to face attractiveness judgments.

28 Little A.C.

Burt D.M.

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Little A.C. The health of a nation predicts their mate preferences: cross-cultural variation in women’s preferences for masculinized male faces. 31 Little A.C.

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Perrett D.I. Putting beauty back in the eye of the beholder. Previous evidence has indicated that preferences for particular faces or face characteristics are shaped by a range of factors, including personality preferences [], the rater’s own facial characteristics [], features of the socioeconomic and cultural environment [], previous visual experience [], and history of social learning []. Individual preferences for faces are also correlated among friends and spouses []. In our sample, most of the variations in face preferences were explained by the contribution of unshared environment—those aspects of the environment that are unique to individuals and not shared between twins. Our data suggest that individual life history and experience are a driving force behind individual face preferences [].

45 McEvoy B.P.

Montgomery G.W.

McRae A.F.

Ripatti S.

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et al. Geographical structure and differential natural selection among North European populations. 46 Turkheimer E.

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Gottesman I.I. Socioeconomic status modifies heritability of IQ in young children. Does this mean that shared environments are not important for individual face preferences? Not necessarily. Our study was conducted with a relatively homogeneous sample of Australian twins []. Given the sociocultural homogeneity of our sample, the low contribution of genetic variance to face IP is particularly noteworthy: estimates of genetic contributions tend to be higher where environments are less variable [].