Facial attractiveness and preference of sexual
dimorphism: A comparison across five populations
Vojtěch Fiala1* , Vít Třebický1,2, Farid Pazhoohi3, Juan David Leongómez4, Petr Tureček1,
S. Adil Saribay5, Robert Mbe Akoko6and Karel Kleisner1
Department of Philosophy and History of Science, Faculty of Science, Charles University, Viničná 7, 128 44 Prague, Czech
Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic,
Department of Psychology,
University of British Columbia, 2136 West Mall, Vancouver, British Columbia, V6T 1Z4, Canada,
Laboratory, Faculty of Psychology, Universidad El Bosque, Bogota, Colombia,
Department of Psychology, Kadir Has
University, Istanbul, Turkey and
Department of Communication and Development Studies, University of Bamenda,
*Corresponding author. E-mail: email@example.com
Despite intensive research, evolutionary psychology has not yet reached a consensus regarding the associ-
ation between sexual dimorphism and attractiveness. This study examines associations between perceived
and morphological facial sexual dimorphism and perceived attractiveness in samples from five distant coun-
tries (Cameroon, Colombia, Czechia, Iran and Turkey). We also examined possible moderating effects of
skin lightness, averageness, age, body mass and facial width. Our results suggest that in all samples, women’s
perceived femininity was positively related to their perceived attractiveness. Women found perceived mas-
culinity in men attractive only in Czechia and Colombia, two distant populations. The association between
perceived sexual dimorphism and attractiveness is thus potentially universal only for women. Across
populations, morphological sexual dimorphism and averageness are not universally associated with either
perceived facial sexual dimorphism or attractiveness. With our exploratory approach, results highlight the
need for control of which measure of sexual dimorphism is used (perceived or measured) because they affect
perceived attractiveness differently. Morphological averageness and sexual dimorphism are not good predic-
tors of perceived attractiveness. It is noted that future studies should use samples from multiple populations
to allow for identification of specific effects of local environmental and socioeconomic conditions on
preferred traits in unmanipulated local facial stimuli.
Keywords: Human face; skin luminance; sexual dimorphism; averageness; geometric morphometrics
Social media summary: Morphological sexual dimorphism is not universally associated with per-
ceived facial sexual dimorphism and attractiveness
According to the signalling theory, facial traits which are perceived as attractive are considered honest
cues of biological fitness (Gangestad & Scheyd, 2005; Thornhill & Gangestad, 1999;Kościński, 2007,
2008), in particular healthiness and viability (Henderson et al., 2016; Rhodes et al., 2003), hormone-
based development of secondary sexual characteristics and fertility (Law Smith et al., 2006; Rantala
et al., 2012; Whitehouse et al., 2015). They also provide specific cues to psychological characteristics
that are important in partnership and childbearing, such as faithfulness (Boothroyd et al., 2008) and
willingness to cooperate (Stirrat & Perrett, 2010).
© The Author(s), 2021. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction
in any medium, provided the original work is properly cited.
Evolutionary Human Sciences (2021), 3, e38, page 1 of 24
Although there is some variation in the perception of facial attractiveness between individuals
belonging to the same local population (Bronstad & Russell, 2007; Germine et al., 2015; Hönekopp,
2006; Kramer et al., 2018), people from similar cultural backgrounds tend to perceive facial attractive-
ness similarly (Kowner & Ogawa, 1995; Langlois et al., 2000; Little & Hancock, 2002; Strzałko &
While there is some evidence that supports a hypothesis of cross-cultural consensus on attractive-
ness ratings (Burke et al., 2013; Coetzee et al., 2014; Langlois et al., 2000), a growing number of studies
report mixed results regarding agreement between samples from distant countries (Apicella et al.,
2007; Jones & Hill, 1993; Sorokowski, Kościński, et al., 2013; Zebrowitz et al., 2012). It is not, however,
the case that members of distant populations either agree or disagree entirely on which traits are per-
ceived as attractive. Instead, some facial traits are preferred across distant samples while others are not.
For example, youthfulness (Buss, 1989; Maestripieri et al., 2014; McLellan & McKelvie, 1993) and
average facial traits (Deffenbacher et al., 1998; Komori et al., 2009; Rhodes et al., 2001; cf. Apicella
et al., 2007) are preferred universally.
Aside from that, there is a cross-cultural agreement concerning preferences regarding facial skin
colouration. Lighter-skinned women are perceived as more attractive than darker-skinned women
within a given population (Aoki, 2002; Badaruddoza, 2007; Carrito et al., 2016; Dixson et al., 2010;
van den Berghe & Frost, 1986; Wagatsuma, 1967), but for some dark-skinned populations, the results
are less conclusive (Dixson et al., 2017; Sorokowski, Sorokowska, et al., 2013). Moreover, in a cross-
cultural comparison, skin colouration variance is an important trait for ratings of ethnic typicality
and attractiveness for African raters, while it is less important for ratings made by Europeans regard-
less of the ethnic origin of the presented stimulus faces (Coetzee et al., 2014; Strom et al., 2012;
Kleisner et al., 2017).
On the other hand, preference for facial sexual dimorphism (how well the development of facial
shape and colouration represents features typical for a given sex) varies substantially across popula-
tions from distant countries, especially with respect to male faces (DeBruine, Jones, Crawford,
et al., 2010; Marcinkowska et al., 2019, and citations below).
Such differences in attractiveness perception between populations from distant countries contradict
the assumption that facial attractiveness serves as a cue of biological quality. A plausible evolutionary-
based explanation is that in populations that live in different environments, preferred traits may vary
because different characteristics are optimal for survival and reproduction under different environ-
mental conditions (DeBruine, Jones, Crawford, et al., 2010; Lee & Zietsch, 2011; Little et al., 2007).
1.1. Morphological and perceived sexual dimorphism and attractiveness
During ontogeny, the facial traits of men and women gradually diverge owing to the action of sex
steroids (Marečková et al., 2011; Whitehouse et al., 2015). As a result, adult faces acquire sexually
dimorphic features (Hausman, 1999; Mooradian et al., 1987; Worthman, 1995).
Higher levels of perceived feminine characteristics in women’s faces are associated with higher per-
ceived attractiveness, as evidenced by previous studies that used non-manipulated women’s faces (Foo,
Simmons, et al., 2017; Muñoz-Reyes et al., 2015; Scott et al., 2010), manipulated composite female
facial stimuli (Perrett et al., 1994,1998; Rhodes et al., 2000; Smith et al., 2009) and even manipulated
individual women’s faces (Mogilski & Welling, 2017; for a review, see Rhodes, 2006). Women with
more sex-typical (more feminine) facial features were also shown to have relatively higher oestrogen
levels (Durante & Li, 2009; Law Smith et al., 2006; Probst et al., 2016). There is evidence from a
US sample to the effect that fertility is positively associated with oestrogen levels (Lipson & Ellison,
1996). On the other hand, in deprived, poorer and rural populations –where women generally
have lower sex hormone levels –fertility is relatively high (Vitzhum, 2009; Vitzhum et al., 2002). It
has also been shown that the preference for femininity in women’s faces is weaker in deprived popula-
tions and populations with worse health indices (De Barra et al., 2013; Marcinkowska et al., 2014;
Penton-Voak et al., 2004).
2 Vojtěch Fiala et al.
With respect to preference for men’s facial sexual dimorphism, the evidence is mixed. Some studies
report preferences for less masculine features in men’s faces (Perrett et al., 1998; Rhodes et al., 2000),
others show preferences for more masculine features (Foo, Simmons, et al., 2017; Johnston et al., 2001;
Peters et al., 2008; Skrinda et al., 2014). A number of studies found preferences for neither masculine
nor feminine features in male faces (Mogilski & Welling, 2017; Penton-Voak & Chen, 2004; Scott
et al., 2010; Stephen et al., 2012). It has been suggested that methodological differences in stimuli
manipulation are at least in part responsible for such mixed results (Rennels et al., 2008; Rhodes,
2006), but it is also possible that distant populations actually differ in preferred male facial traits.
The usual approach to addressing this variation in results is to test adaptive hypotheses on fitness out-
comes of preference of certain traits across various populations (Brooks et al., 2011; DeBruine et al.,
2011; DeBruine, Jones, Crawford, et al., 2010).
Masculine features may be cues to health. Men with masculine facial features have been considered
more immunocompetent (Foo, Nakagawa, et al., 2017; cf. Nowak et al., 2018; Rantala et al., 2012) and
healthier than their less masculine peers (Rhodes et al., 2003; Thornhill & Gangestad, 2006; see also
Moore et al., 2011; Rantala et al., 2012). It is thus hypothesised that by preferring more masculine men,
women try to increase their chances of acquiring a healthier mate who would have more immunocom-
Moreover, formidability and resource holding potential, i.e. an individual’s willingness to engage in
conflict over resources stemming from his/her ability to obtain or withdraw resources from a rival
(Třebický et al., 2019), are also cued by masculine facial features (Boothroyd et al., 2007; Carre &
McCormick, 2008; Stirrat & Perrett, 2010; Swaddle & Reierson, 2002;Třebický et al., 2015).
Women who are endangered by unequal resource distribution (e.g. owing to economic inequality)
may prefer more masculine men because such men are more likely to obtain resources for their fam-
ilies (Brooks et al., 2011; Little et al., 2013). Moreover, the preference for masculine men seems adap-
tive because a formidable and dominant partner can better protect his mate against violence and harm
(Ryder et al., 2016).
Nevertheless, masculinity is also linked to the risk of testosterone-associated antisocial behaviours
(van Bokhoven et al., 2006), higher divorce probability (Booth et al., 1993; Mazur & Michalek, 1998)
and low partner fidelity (Penton-Voak & Chen, 2004; Polo et al., 2019), which may all have a negative
impact on parental investment. All in all, women seem to face a trade-off between choosing a mascu-
line, immunocompetent and formidable mate, who could also harm and/or leave her, or a less mas-
culine but cooperative and more nurturing mate. Such a trade-off may lack a universal solution across
populations from distant countries.
1.2. The current study
Research on differences in the association between sexual dimorphism and perceived attractiveness
usually builds on a single set of manipulated facial stimuli, which are then used for a number of
sets of raters from distant populations in a hypothesis-driven research paradigm (DeBruine, Jones,
Crawford, et al., 2010; Marcinkowska et al., 2019; Scott et al., 2014). It could be objected, however,
that manipulation of sexual dimorphism may affect stimuli features in a way that need not be ecologic-
ally relevant to all of the investigated populations. Moreover, evaluation of faces by raters from a visu-
ally distinctive population may cause people to perform worse on trait attribution (Anzures et al.,
2013). The use of local faces therefore has clear advantages.
There are multiple evolutionary-based hypotheses which aim to explain both the consensus and
differences in preferences of various facial traits across people from distant populations (see above).
Moreover, it is not clear whether raters across distant populations prefer similar traits in local faces
and whether they interpret sexually dimorphic facial traits similarly.
We have therefore conducted a set of non-confirmatory analyses (following Scheel et al., 2020; see
also Nakamura & Watanabe, 2019) on the association between perceived facial attractiveness, per-
ceived and measured sexual dimorphism and other facial traits across populations from five distant
Evolutionary Human Sciences 3
countries (Cameroon, Colombia, Czechia, Iran and Turkey). In each country, we used only facial stim-
uli collected within the local population. Using various facial feature metrics and path analysis, we also
explored sample-specific patterns of serial and parallel mediation among the predictors of perceived
characteristics. Specifically, we used predictors that may affect perceived attractiveness, perceived sex-
ual dimorphism and measured sexual dimorphism, and mediate their relationship, namely morpho-
logical averageness, skin lightness, age, body mass index (BMI) and facial width to height ratio
(fWHR) (see Supplementary Material, Sections S1.1–S1.4).
To avoid any confounding effects of stimuli manipulation on trait attribution (DeBruine, Jones,
Smith, et al., 2010; see also Kleisner et al., 2019; Rennels et al., 2008), we used unmanipulated facial
stimuli. Moreover, we used two measures of facial sexual dimorphism: (a) perceived sex-typicality
(perceived femininity in women and perceived masculinity in men); and (b) sexual shape dimorphism
of facial shape calculated from landmark-based geometric morphometrics. According to a recent
study, these two measures of facial sexual dimorphism are only moderately correlated (Mitteroecker
et al., 2015), presumably because perceived sex-typicality is also affected by skin lightness across popu-
lations (Carrito & Semin, 2019; van den Berghe & Frost, 1986).
Although this study is of an exploratory nature, we made several predictions (see Table 3a): based
on previous studies on the association between perceived and morphological sexual dimorphism
(Komori et al., 2011; Mitteroecker et al., 2015), we expect that perceived and measured sexual
dimorphism will be correlated in every sampled population positively, albeit weakly (r≈0.3).
Moreover, morphological averageness of facial configurations should be moderately positively
associated with perceived attractiveness (Jones & Jaeger, 2019).
Skin lightness may affect perceived sex-typicality and attractiveness mainly in Cameroon, given that
people of sub-Saharan African origin are more sensitive to facial skin colouration and lightness
variability as a cue for both facial attractiveness (Kleisner et al., 2017) and ethnic typicality (Strom
et al., 2012). Moreover, skin lightness has also been associated with perceived male sex-typicality and
healthiness in previous research based on people of European origin (Carrito & Semin, 2019). Skin
lightness should therefore negatively relate to perceived masculinity in male faces in all five samples.
We further predict that perceived femininity of women’s faces will be associated with higher ratings
of attractiveness across all five population samples (Cameroon, Colombia, Czechia, Iran and Turkey)
regardless of their mutual distance and eventual cultural differences. It has been demonstrated that
perceived sex-typicality of women is an important component of their perceived facial attractiveness.
We therefore predict a strong association between perceived attractiveness and perceived femininity in
women in samples from all of the five populations (r≈0.8; see Foo, Simmons, et al., 2017; Koehler
et al., 2004).
Concerning the preference for male sex-typicality, there is substantial disagreement across popula-
tions from distant countries (Marcinkowska et al., 2019) and even between same-country samples
recruited from or primed to different socioeconomic conditions (DeBruine et al., 2011; Little,
Cohen, et al., 2007). This suggests the existence of various factors that may shape masculinity prefer-
ences uniquely in each country. We sampled populations from only five countries, which is why we
have only five data points on the cross-population level and therefore cannot anticipate which of those
forces would drive preferences in our samples. All predictions are summarised in Table 3a.
For detailed descriptions of stimuli acquisition, measurements, rating procedures and analyses, see
Supplementary Materials in the OSF entry for this study at https://osf.io/va8pg/?view_only=021e113
This study was approved by the Institutional Review Board of Charles University, Faculty of
Science. The photographed individuals and raters were informed about the purpose of data collection.
Photographed individuals signed informed consent and raters consented by clicking ‘I agree’in the
4 Vojtěch Fiala et al.
2.1. Facial photos acquisition
As stimuli, we collected a total of 709 standardised frontal facial photographs (357 men and 352
women) from five countries (Cameroon, Colombia, Czechia, Iran and Turkey). Data about age, height
and body weight were collected from participants in Cameroon, Colombia, Czechia and Turkey, while
only information about age was collected from Iranian participants. We used a pre-existing available
photograph dataset of Iranian faces that has been acquired prior to the decision to use those photo-
graphs in this research and which lacked the information on the height and weight of the models. The
effect of relative weight therefore could not be tested in the Iranian sample. The number of stimuli per
sample and sex and samples’descriptives are summarised in Table 1.
The Cameroonian stimuli were collected in two separate runs (2013 and 2016) and these two sets
were also rated separately. For the purpose of this study, the two Cameroonian samples were com-
bined, but analyses of the separate samples yielded results similar to analyses of the combined sample
(the alternative analyses are presented in Table S3 and Figure S5 in the Supplementary Material.).
The photographs were taken with digital cameras, using external light sources and homogeneous
white or grey backgrounds. Lighting conditions were not standardised across the samples but were
uniform within each sample (each country). All participants were asked to remove their glasses, facial
jewellery, other adornments or cosmetics, adopt a neutral facial expression and look directly into the
camera. All women in the Iranian set wore a hijab that covered hair, ears and part of their cheeks.
All photographs were adjusted to set the eyes horizontally at the same height and leave approximately
the same length of neck visible. They were subsequently cropped and exported at ∼500 × 700 px reso-
lution; see Section S2.1 in the Supplementary Material for further details of photo acquisition.
The stimuli were sampled by convenience and they do not represent the populations as a whole.
Nonetheless, each sample represents similar social strata within the society (young to middle-aged people,
mostly university students, academic staff and members of the general public whowere willing to ‘help the
science’). The composition of the samples was therefore comparable across the sampled populations.
2.2. Rating sessions
Raters from each country assessed only stimuli from their own country. The ratings were self-paced and
took part online (except for attractiveness of Cameroonian 2013 stimuli) using participants’own elec-
tronic devices. Stimuli appeared on the screen in a pseudo-randomised order. The questionnaires were
in English (Cameroon), Spanish (Colombia), Czech (Czechia), Farsi (Iran), and Turkish (Turkey).
Raters were not compensated for their participation. Attractiveness was rated only by opposite-sex raters
except for Turkey, where both male and female facial stimuli were rated by raters of both sexes (for
alternative analyses with the subset of opposite-sex raters, see Table S4 and Figure S5 in the
Supplementary Material). Perceived masculinity and femininity in the Cameroonian, Colombian, and
Turkish sample were rated by raters of both sexes, while in Czechia and Iran, perceived masculinity
and femininity were rated only by persons of the opposite sex. The number of stimuli in each country
is reported in Table 1. The number of raters in each category and associated descriptive statistics are
summarised in Table 2 and in Table S1 in the Supplementary Material. A detailed description of the
rating process is in the Supplementary Material (Section S2.2, ‘Acquisition and processing of ratings’).
Cameroonian stimuli from 2013 and 2016 were rated in two separate runs. Of all the datasets, only
the perceived attractiveness of Cameroonian stimuli collected in 2013 was not rated using Qualtrics. It
was rated in an offline purpose-made ‘ImageRater’program visually similar to the Qualtrics interface.
Cameroonian raters assessed the stimuli on a verbally anchored seven-point Likert scale (1, not at all
attractive/masculine/feminine; 7, very attractive/masculine/feminine face). Each rater rated all the
stimuli within a given set. Raters were mostly university students who resided in towns and villages
of western Cameroonian provinces.
In Colombia, raters were recruited by JDL and his co-workers. They were mostly university students
from Bogota, D.C. The sample consisted of 997 raters (565 women). They rated only a randomly
Evolutionary Human Sciences 5
Table 1. Descriptive statistics of the stimuli sample
Sample NAge (mean ± SD) BMI (mean ± SD) CIELab L* (mean ± SD) fWHR (mean ± SD) SShD (mean ± SD)
Cameroonian women 100 22.01 ± 3.87 24.79 ± 4.56 38.88 ± 5.55 2.13 ± 0.16 −0.0119 ± 0.0147
Cameroonian men 100 22.00 ± 2.61 23.11 ± 2.09 34.65 ± 5.23 2.10 ± 0.16 0.0119 ± 0.0130
Colombian women 66 20.71 ± 2.77 22.65 ± 3.15 67.37 ± 4.05 2.00 ± 0.12 −0.0206 ± 0.0221
Colombian men 72 20.38 ± 2.42 22.92 ± 2.80 58.72 ± 5.31 1.99 ± 0.13 0.0189 ± 0.0258
Czech women 50 23.64 ± 4.33 22.16 ± 2.90 60.26 ± 4.27 1.95 ± 0.13 −0.0189 ± 0.0185
Czech men 50 24.04 ± 3.92 22.38 ± 2.27 58.16 ± 2.32 1.89 ± 0.13 0.0189 ± 0.0156
Iranian women 43 20.63 ± 1.09 NA 65.62 ± 3.74 1.78 ± 0.11 −0.0199 ± 0.0210
Iranian men 44 20.64 ± 1.60 NA 57.33 ± 5.76 1.81 ± 0.14 0.0195 ± 0.0173
Turkish women 93 21.20 ± 1.51 20.75 ± 2.92 80.55 ± 2.74 1.97 ± 0.11 −0.0223 ± 0.0148
Turkish men 91 21.52 ± 1.95 23.64 ± 3.51 77.90 ± 3.36 2.01 ± 0.13 0.0231 ± 0.0153
Sample NAvrg (mean ± SD) Fem/masc (mean ± SD) Fem/masc (ICC) Attr (mean ± SD) Attr (ICC)
Cameroonian women 100 0.0582 ± 0.0131 4.67 ± 0.70 0.94/0.97
3.08 ± 0.65 0.91/0.96
Cameroonian men 100 0.0571 ± 0.0121 5.52 ± 0.59 0.95/0.96
3.38 ± 0.76 0.92/0.96
Colombian women 66 0.0523 ± 0.0121 4.77 ± 1.09 0.99 (ICC 1,k) 3.26 ± 0.90 0.99 (ICC 1,k)
Colombian men 72 0.0561 ± 0.0168 4.84 ± 0.75 0.98 (ICC 1,k) 2.22 ± 0.54 0.98 (ICC 1,k)
Czech women 50 0.0542 ± 0.0128 3.86 ± 0.86 0.97 2.68 ± 0.86 0.97
Czech men 50 0.0544 ± 0.0118 4.07 ± 0.80 0.99 2.78 ± 0.87 0.99
Iranian women 43 0.0556 ± 0.0134 3.52 ± 0.55 0.92 2.03 ± 0.44 0.93
Iranian men 44 0.0585 ± 0.0152 4.34 ± 0.58 0.90 1.92 ± 0.46 0.92
Turkish women 93 0.0488 ± 0.0105 3.93 ± 0.81 0.95 (ICC 1,k) 2.89 ± 0.94 0.96 (ICC 1,k)
Turkish men 91 0.0526 ± 0.0113 4.44 ± 0.67 0.94 (ICC 1,k) 2.41 ± 0.76 0.95 (ICC 1,k)
fWHR, Facial width to height ratio; BMI, body mass index; CIELab L, skin lightness; SShD, sexual shape dimorphism (measured facial sexual dimorphism); Avrg, morphometrical averageness; Fem/masc, perceived
femininity(women)/masculinity(men); Attr, perceived attractiveness; SD, standard deviation; ICC, intraclass correlation coefficient (measure of inter-rater agreement), ICC (3,k) if not otherwise stated.
Cameroonian samples from 2013 and 2016 were rated separately, ICCs are as follows: 2013 Sample/2016 Sample
6 Vojtěch Fiala et al.
Table 2. Descriptive statistics of the rater sample
Sample Rated attribute
raters Raters’sex Raters’age (mean ± SD) Raters’weight Raters’height
Cameroon 2013 Women attractiveness 34 Men 22.21 ± 3.14 NA NA
Men attractiveness 28 Women 22.11 ± 3.89 NA NA
77 Men and women 24.00 ± 4.12 66.62 ± 11.23 165.10 ± 7.75
Cameroon 2016 Women attractiveness 49 Men 22.96 ± 3.23 68.84 ± 8.73 169.70 ± 8.35
Men attractiveness 51 Women 23.37 ± 4.25 65.18 ± 11.39 161.10 ± 5.32
94 Men and women 22.99 ± 3.00 64.82 ± 9.53 164.50 ± 7.76
Colombia Women attractiveness 432 Men 22.01 ± 3.92 NA NA
Men attractiveness 565 Women 21.85 ± 4.81 NA NA
Women femininity 432 Men 22.01 ± 3.92 NA NA
Men masculinity 565 Women 21.85 ± 4.81 NA NA
Czechia Women attractiveness 33 Men 28.18 ± 4.21 NA 182.50 ± 6.35
Men attractiveness 89 Women 27.56 ± 4.23 NA 169.00 ± 5.50
Women femininity 44 Men 33.00 ± 9.68 86.23 ± 12.23 182.40 ± 7.67
Men masculinity 231 Women 32.04 ± 7.59 67.74 ± 15.32 168.30 ± 6.46
Iran Women attractiveness 46 Men 37.30 ± 12.29 85.61 ± 15.13 178.60 ± 7.93
Men attractiveness 41 Women 34.88 ± 9.91 65.73 ± 14.98 165.60 ± 6.07
Women femininity 33 Men 27.73 ± 3.77 78.52 ± 12.93 178.30 ± 5.99
Men masculinity 31 Women 29.35 ± 4.54 59.68 ± 9.20 164.60 ± 6.66
Turkey Women attractiveness 1207
862 women; 225
All, 22.09 ± 3.66; women, 21.71 ± 2.80;
men, 23.53 ± 5.69
All, 61.6 ± 12.33; women,
58.02 ± 9.66; men,
75.23 ± 11.90
All, 167.90 ± 7.79;
women, 165.4 ± 7.79;
men, 177.70 ± 6.09
In the Cameroonian and Turkish samples, perceived sex-typicality (masculinity of men, femininity of women) was rated by a combined set of male and female raters.
Some Turkish raters did not report their attributes (age, sex, weight and height). For alternative analyses without those raters and split by raters’sex, see Table S4 and Figure S5 in the online Supplementary
Evolutionary Human Sciences 7
chosen opposite-sex subset (N= 20) of the stimuli. The rating scale ranged from 1.0 to 10.0 (with one
decimal place). The endpoints were also anchored verbally, with 1.0 not at all attractive/masculine/
feminine and 10.0 very attractive/masculine/feminine face. All participants identified themselves as
Czech raters were recruited by fliers in university buildings, asked face to face or by email by two of
the authors (VF, KK) and KK’s other co-workers. The link was also shared via social networks through
groups of Charles University students and academic staff. Only raters who identified themselves as
heterosexual and completed the whole questionnaire were entered into the analysis.
Iranian raters (see Section S.2.2.4 in the online Supplementary Material) were recruited via email
with a link to the questionnaires sent by one of the authors (FP), who also translated the original ques-
tionnaires from English into Farsi. Raters who identified themselves as homosexual, did not complete
the entire questionnaire or rated all stimuli identically were excluded from subsequent analyses.
In Turkey, the rating data were collected online by one of the authors (AS). Participants were asked to
rate a subset of the Turkish face database (Bogazici Face Database; see Saribay et al., 2018). Students
from the Bogazici University were deliberately excluded from the pool of potential raters owing to
potential bias stemming from familiarity with the stimuli. Raters saw only a subset of stimuli (N=8
faces per rated attribute) in pseudorandomised order. They rated both the male and female stimuli.
2.3. Facial measures
2.3.1. Skin lightness
In the Cameroonian (2013), Colombian, Iranian and Turkish targets, we measured skin lightness from
facial photographs using ImageJ software (Schneider et al., 2012) with the plugin Color Transformer 2.
In the Cameroon 2016 and Czech sample, facial skin lightness was measured in vivo with a spectro-
photometer (Ocean Optics Flame-S, 200–850 nm, optical resolution 2 nm). All measurements were
taken on three patches of the target face (forehead, left and right cheek) and expressed as the L*
dimension of CIELab colour space (Hunter, 1958). According to Coetzee et al. (2012), such difference
in the lightness measurement (in vivo by spectrophotometer vs. using facial photographs) should not
affect the results. Nevertheless, we also ran the analysis with L* measured from photos in ImageJ in
samples where lightness was originally based on in vivo spectrophotometric measurements. Results
for these analyses are available in the Supplementary Material (Table S5 and Figure S6). The results
were not substantially affected by the method of skin lightness measurement.
2.3.2. Relative facial width
We measured bizygomatic facial width and upper facial height from the glabella to the border of the
upper lip from facial photographs of the stimuli persons (Třebický et al., 2015). Then we calculated the
fWHR as facial width divided by facial height (see also Section S2.4.2 in the Supplementary Material
for a detailed description of the measurement).
2.3.3. Facial shape analysis
We manually landmarked each facial photograph with 72 landmarks (36 landmarks and 36 semiland-
marks) in tpsDig2 software, version 2.31 (Rohlf, 2015). We followed the definitions of standard land-
marks positions previously used by Kleisner et al. (2019). Procrustes superimposition of all landmark
configurations was done using the ‘gpagen()’function in the R package Geomorph (Adams &
Otárola-Castillo, 2013). Semilandmark positions were optimised based on minimising the Procrustes dis-
tances between corresponding points. For a more detailed description of facial shape analysis, see Section
S.2.4.4 of the Supplementary Material.
We computed facial morphological averageness as each face’s distance from the average facial
configuration of its bearer’s sex and population sample (e.g. averageness of Turkish male targets).
The higher the value, the less average the facial configuration.
8 Vojtěch Fiala et al.
Next, we computed the level of facial sexual shape dimorphism (morphological sexual dimorphism)
by projecting each facial configuration on a vector connecting the male and female average within a
given sample (e.g. Turkish male and female targets –the scale uses both sexes within a given popu-
lation sample). Higher negative values of sexual shape dimorphism indicate a more female-like facial
shape, while higher positive values indicate a more male-like facial shape.
Note that all Iranian women in our sample wore the hijab. To compute the averageness of Iranian
women and sexual shape dimorphism of Iranian men and women, we used the configuration of 49
innermost facial landmarks. Computations of morphological facial averageness and sexual shape
dimorphism in the remaining groups were always based on all 72 landmarks.
2.4. Statistical analysis
All analyses were conducted using the R software for statistical computing (version 3.6.0; R Core
Team, 2020). All datasets and R script are available at https://osf.io/va8pg/?view_only=021e113218
To assess interrater agreement on perceived attractiveness and sex-typicality, we computed
Intraclass correlations using the ‘ICC()’function of the ‘psych’package (Revelle, 2018). We ran two-
way mixed average score Intraclass correlations (3,k) for the Cameroonian, Czech and Iranian datasets
where raters saw all targets. Raters in Colombia and Turkey saw only a subsample of the relevant tar-
gets, which is why for these two samples, we ran one-way random, average score Intraclass correlations
(1,k) (Shrout & Fleiss, 1979). All intraclass correlations were very high (ICC > 0.9, see Table 1). For
subsequent analyses, we calculated mean perceived attractiveness and perceived sex-typicality ratings
for each photographed person.
Two-tailed Pearson’s correlation coefficients (and their 95% confidence intervals) were used to inves-
tigate bivariate associations between all collected variables. The resulting p-values were adjusted for
multiple comparisons using the Benjamini–Hochberg correction procedure. Unlike the Bonferroni
correction, which controls for familywise error rate, Benjamini–Hochberg correction controls for the
predicted (expected) proportion of errors among rejected null hypotheses, that is, the false discovery
rate (Benjamini & Hochberg, 1995). It is therefore better suited for a non-confirmatory analysis
where there is no dependence between multiple comparisons. The results of performed correlations
and descriptive statistics (mean, SD, and range) for all variables are presented in Tables S2–S5 in the
Supplementary Material. The associations were further visualised using heatmaps of Pearson correlation
coefficients (see Figure S4 in Supplementary Material) using the ‘col_fun()’function of the circlize pack-
age (Gu et al., 2014) and the ‘Heatmap()’function of the ComplexHeatmap package (Gu et al., 2016)in
R software, and subsequently edited in InkScape version 0.92.4. To visualise the strength of associations
between measures of sexual dimorphism (SShD and perceived sex-typicality) and perceived attractive-
ness across the five samples, we created a forest plot (see Figure 1) using the ‘forestplot()’function of
the forestplot package (Gordon & Lumley, 2020).
To explore causal relationships between age, skin lightness, fWHR, BMI, averageness, morpho-
logical sexual shape dimorphism, perceived sex-typicality and perceived attractiveness, we ran path
analyses with ‘sem()’function from the lavaan package for R (Rosseel, 2012). We conducted a separate
path analysis for each stimulus sex/sample combination (e.g. Iranian female stimuli). Perceived sex-
typicality and attractiveness were set as dependent variables (see Figure 2), while directionality of
mutual interdependence between the two was not decided and we treated it as a correlation; for a
detailed description of the model development, see Section S.2.5 in the Supplementary Material.
Paths designated in this study were based on evidence from literature, formal logic and our decisions
(see also Kane & Ashbaugh, 2017). The aim of the study was to explore the proposed paths, not to
confirm a pattern as an objective causality. Because the number of observations per estimated param-
eter was relatively low, robust p-values were obtained using a permutation test with 10,000 iterations,
where the full models were estimated on randomised datasets.
Evolutionary Human Sciences 9
Figure 1. Forest plots displaying the relative strength of Pearson’s correlations between perceived attractiveness and perceived
sex-typicality (a), perceived attractiveness and sexual shape dimorphism (b) and between sexual shape dimorphism and perceived
sex-typicality (c), with confidence intervals of each coefficient. Each row corresponds to a single sample (women from all five sam-
ples, men from all five samples, with sampled countries in alphabetical order). Blue circles represent Pearson’s correlation coeffi-
cients (mean estimate on the given sample), while black lines stand for error bars defined as 95% confidence intervals around each
mean correlation. A vertical line in zero (‘0’) enables us to inspect whether the confidence interval for a given correlation contains
zero. The columns on the right side of the diagrams show coefficients of the associations. This figure facilitates a comparison of
bivariate associations among the population samples.
10 Vojtěch Fiala et al.
3.1. Correlational analyses
In women, we found a significant positive correlation between perceived attractiveness and perceived
femininity in all five samples: Cameroonian, r(98) = 0.77; 95% CI [0.68, 0.84], p< 0.001; Colombian,
Figure 2. A visualisation of path ana-
lyses (multiple regression models)
between the rated facial attributes
(perceived sex-typicality and attract-
iveness) and facial measures ordered
by the sex of the stimuli. Arrows
represent the direction of the associ-
ation. Non-significant paths are omit-
ted. Association between perceived
sex-typicality and attractiveness was
treated as a correlation (i.e. the direc-
tion was not specified). Numbers
next to the paths indicate the esti-
mate of regression or correlation
coefficient in a corresponding model
with standardised variables. Red
colour denotes a negative coefficient.
The graph shows to what extent is
the observed within-sample variabil-
ity of each variable explained by
other variable(s). In every sample,
perceived femininity and attractive-
ness are closely mutually associated
in the women’s samples. In most
population samples, perceived
masculinity was not associated with
perceived men’s attractiveness. The
significant paths mostly replicate sig-
nificant Pearson’s correlations (see
Figure S4) (+ p< 0.10, *p< 0.05,
** p< 0.01, *** p< 0.001).
Evolutionary Human Sciences 11
r(64) = 0.84 [0.76, 0.90], p< 0.001; Czech, r(48) = 0.87 [0.78, 0.92], p< 0.001; Iranian, r(41) = 0.93
[0.88, 0.96], p< 0.001; and Turkish, r(92) = 0.89 [0.84, 0.93], p< 0.001. Furthermore, in the sample
of Cameroonian women, both perceived attractiveness and perceived femininity were significantly
and positively correlated with skin lightness (r(98) = 0.31 [0.12, 0.47], p= 0.014; and r(98) = 0.36
[0.17, 0.52], p= 0.004, respectively). In the rest of women’s samples, the correlation between skin
lightness and perceived femininity or attractiveness was not significant ( p> 0.05, p-value after
Benjamini–Hochberg correction; see Figure S4).
In men, perceived masculinity and attractiveness were significantly and positively correlated only in
the Colombian and Czech sample (r(70) = 0.44 [0.23, 0.61], p= 0.004; r(48) = 0.48 [0.23, 0.67], p=
0.013, respectively). In samples of the Cameroonian, Iranian and Turkish male faces, perceived mas-
culinity was significantly negatively associated with facial skin lightness (r(98) = −0.38 [−0.54, −0.20],
p= 0.001; r(42) = −0.63 [−0.78, −0.40], p< 0.001; r(89) = 0.32 [−0.49, −0.12], p= 0.009, respectively),
meaning that darker men were perceived as more masculine.
Morphological averageness was significantly correlated with perceived attractiveness in Iranian men
(r(42) = −0.41 [−0.63, −0.13], p= 0.028), with perceived femininity (r(48) = −0.44 [−0.64, −0.19], p=
0.011) and attractiveness (r(48) = −0.52 [−0.70, −0.28], p= 0.002) in Czech women, and with per-
ceived femininity (r(92) = −0.29 [−0.46, −0.09], p= 0.03) and attractiveness (r(92) = −0.34 [−0.51,
−0.14], p= 0.007) in Turkish women. In these samples, more average faces (i.e. ‘averageness’values
closer to zero) were thus perceived as more sex-typical and/or attractive.
Finally, sexual shape dimorphism was significantly correlated with perceived masculinity in the
sample of Cameroonian men (r(98) = 0.33 [0.15, 0.50], p= 0.005), meaning that men with more male-
like facial shape were perceived as more masculine. Sexual shape dimorphism also correlated with per-
ceived femininity in Colombian women (r(64) = −0.33 [−0.53, −0.10], p= 0.045), implying that
women with more female-like facial shape were perceived as more feminine, with perceived femininity
(r(41) = −0.45 [−0.66, −0.18], p= 0.016) and attractiveness (r(41) = −0.43 [−0.65, −0.15], p= 0.022)
in Iranian women, and with perceived masculinity of Turkish men (r(89) = 0.44 [0.26, 0.59], p<
0.001). In the rest of the samples, the association between sexual shape dimorphism and perceived
sex-typicality was not significant after Benjamini–Hochberg correction (see Figure S4). Figure 1 pre-
sents forest plots for correlations between perceived sex-typicality, sexual shape dimorphism and per-
ceived attractiveness for each population sample and sex. All correlation coefficients are reported in
detail in Figure S4 and Table S2–S5 in the Supplementary Material.
3.2. Path analyses
3.2.1. Women’s faces
Significant paths among variables mostly replicated the significant correlations reported above. In all
women’s samples, there were significant positive residual correlations between perceived femininity
and attractiveness (r= 0.75; 95% CI [0.56, 0.94] for Cameroonian, 0.82 [0.57, 1.00] for Colombian,
0.80 [0.59, 1.00] for Czech, 0.91 [0.65, 1.00] for Iranian and 0.89 [0.66, 1.00] for Turkish women,
p< 0.001 in all cases).
In Cameroonian women, there was a significant positive association ( partial regression) between
facial skin lightness and both perceived femininity and attractiveness (β= 0.35 [0.18, 0.52], p=
0.001; β= 0.29 [0.11, 0.47], p= 0.004, respectively).
Concerning correlations between facial morphology and perceived traits, we found a significant
association between sexual shape dimorphism and perceived femininity in the Colombian and
Iranian women’s sample (β=−0.30 [−0.53, −0.07], p= 0.019; β=−0.50 [−0.74, −0.25], p= 0.001,
respectively), while in the Iranian women’s sample, sexual shape dimorphism was significantly asso-
ciated with perceived attractiveness (β=−0.47 [−0.71, −0.22], p= 0.002). More feminine facial morph-
ology had a more negative value of sexual shape dimorphism, which indicates that feminine facial
shape was perceived as more attractive in Iran and as more feminine in the Iranian and Colombian
women’s sample. In Colombia, partial regression between sexual shape dimorphism and perceived
12 Vojtěch Fiala et al.
attractiveness was not significant (as noted above, we report the ‘robust p-values’based on bootstrap-
ping) but was of a similar magnitude and direction (β=−0.25 [−0.49, −0.02], p= 0.051). In the Czech
women’s sample, morphological facial averageness was significantly associated with both perceived
femininity and attractiveness (more average faces were perceived as more feminine and attractive,
β=−0.47 [−0.70, −0.25], p= 0.001; β=−0.53 [−0.74, −0.33], p< 0.001, respectively). The same
held true for the Turkish female sample (β=−0.30 [−0.49, −0.11], p= 0.002 for the association
between morphological averageness and perceived femininity; β=−0.34 [−0.53, −0.15], p< 0.001
between morphological averageness and perceived attractiveness).
Perceived facial attractiveness and perceived femininity in women’s samples were also related to
other variables: in Colombia, women with a higher BMI were perceived as significantly less feminine
(β=−0.28 [−0.52, −0.05], p= 0.026) and less attractive (β=−0.25 [−0.50, −0.01], p= 0.041), while
in the Cameroonian women’s sample, age was significantly negatively associated with perceived
femininity (β=−0.26 [−0.44, −0.08], p= 0.017).
Concerning associations between the predictors themselves, in Cameroonian (β= 0.28 [0.08, 0.47],
p= 0.005), Colombian (β= 0.33 [0.10, 0.56], p= 0.010) and Turkish women’s samples (β= 0.23 [0.04,
0.42], p= 0.029), fWHR was significantly positively associated with BMI. In Cameroon (β= 0.27 [0.08,
0.46], p= 0.008) and Turkey (β= 0.39 [0.21, 0.58], p< 0.001), women with a higher fWHR exhibited
lower levels of female-like sexual shape dimorphism, while in Czechia, women with higher fWHR
exhibited higher levels of female-like SShD (β=−0.33 [−0.59, −0.07], p= 0.025). Age was significantly
associated with BMI (β= 0.24 [0.05, 0.43], p= 0.025) only in the Cameroonian women’s sample and
with fWHR in the Iranian (β= 0.46 [0.19, 0.72], p= 0.002) and Turkish women’s sample (β=−0.27
[−0.46, −0.08], p= 0.007).
3.2.2. Men’s faces
Perceived masculinity of Colombian and Czech male faces was significantly positively correlated with
their perceived attractiveness (r= 0.46 [0.24, 0.67], p< 0.001 and r= 0.62 [0.36, 0.88], p< 0.001,
respectively). In samples from the other populations, the association between perceived attractiveness
and perceived masculinity was not significant (r=−0.07 [−0.22, 0.07], p= 0.57; r= 0.16 [−0.02, 0.33],
p= 0.53; and r= 0.19 [0.03, 0.36], p= 0.14 for Cameroonian, Iranian and Turkish male faces, respect-
ively). Facial skin lightness was significantly negatively associated with perceived masculinity in the
Cameroonian (β=−0.33 [−0.49, −0.18], p= 0.001), Colombian (β=−0.24 [−0.44, −0.03], p=0.041)
and Iranian (β=−0.50 [−0.70, −0.30], p= 0.001) male samples.
Concerning morphological and perceived facial traits, the following significant associations were
observed: in the Cameroonian (β= 0.36 [0.19, 0.54], p< 0.001), Colombian (β= 0.30 [0.07, 0.53],
p= 0.014) and Turkish sample (β= 0.45 [0.24, 0.66], p< 0.001) more male-like facial shapes (sexual
shape dimorphism) were perceived as more masculine (perceived masculinity). In the Iranian
(β=−0.42 [−0.68, −0.15], p= 0.007) and Turkish samples (β=−0.23 [−0.43, −0.04], p= 0.024), facial
configurations closer to the average (morphological averageness) were perceived as more attractive.
Body mass index was significantly positively associated with fWHR in Cameroonian (β= 0.26 [0.07,
0.45], p= 0.009), Colombian (β= 0.23 [0.01, 0.45], p= 0.048), Czech (β= 0.29 [0.01, 0.57], p= 0.041)
and Turkish (β= 0.36 [0.16, 0.55], p< 0.001) men, meaning that relatively heavier men had relatively
wider faces (in the Iranian stimuli, we did not measure weight and height and thus could not compute
this association). In Cameroonian men, fWHR was significantly associated with morphological
averageness (β=−0.24 [−0.43, −0.04], p= 0.019) and sexual shape dimorphism (β= 0.39 [0.21, 0.58],
p< 0.001). The fWHR was also significantly positively related to sexual shape dimorphism in Iranian
(β= 0.57 [0.33, 0.81], p< 0.001) and Turkish men (β= 0.40 [0.22, 0.59], p< 0.001).
See also Figures S5 and S6 in the Supplementary Material for path analyses with alternative vari-
ables and datasets. Table S6 in the Supplementary Material reports the full ‘lavaan’output for all fitted
Evolutionary Human Sciences 13
In this study, we investigated the associations between perceived attractiveness, perceived sex-typicality
and facial sexual shape dimorphism (morphological sexual dimorphism) in samples from five distant
populations, namely Cameroon, Colombia, Czechia, Iran and Turkey. We also explored whether these
associations were affected by other factors (morphological facial averageness, skin lightness, relative
facial width, body mass and age).
As predicted (see Table 3), raters strongly preferred women who were perceived as more feminine
in all samples. On the other hand, raters did not agree on the preferred degree of perceived male sex-
typicality. In fact, only raters from the Czech and Colombian samples preferred men who were
perceived as more masculine. Morphometric variables (facial morphological averageness, sexual
shape dimorphism), relative facial width and measured skin lightness were inconsistent predictors
of perceived scales: they predicted perceived characteristics only in a subset of the samples.
4.1. Association between perceived sex-typicality and perceived attractiveness
The hypothesis according to which preferred visual facial traits should be invariant across samples
from distant populations (Langlois et al., 2000) found support in our results only in part. In all five
female samples, perceived attractiveness and perceived women’s femininity were positively associated.
Concerning preference for femininity, our results thus converge with previous evidence to the effect
that perceived female femininity is preferred across populations (Little et al., 2011), except for cases
where the sampled populations are distant, visually distinctive and/or inhabit various environments
(cf. Marcinkowska et al., 2014).
Such high correlation between perceived femininity and attractiveness conforms to the evolutionary
model and its implicit assumptions. It has been proposed, mostly based on indirect evidence, that fem-
ininity in women’s faces presents an honest cue to proper hormone-based development (Probst et al.,
2016; Thornhill & Grammer, 1999), fertility and reproductive health (Law Smith et al., 2006)and
should be therefore preferred. Moreover, our results across samples replicate findings of recent studies
which identified a strong association between facial femininity and attractiveness using various research
design (Foo, Simmons et al., 2017; Mogilski & Welling, 2017; Muñoz-Reyes et al., 2015; Smith et al.,
2009). Contrary to this assumption of universal preference of female femininity, other scholars sug-
gested that in harsh environments (De Barra et al., 2013; Penton-Voak et al., 2004) and small rural
populations (Scott et al., 2014), preference for female sex-typicality should be weaker or absent
altogether. In our study, however, the magnitude of femininity preference was relatively stable across
the samples from distant populations. Accordingly, female femininity, eventually pointing to sexual
maturity and reproductive health, may present a women’s characteristic that is universally preferred.
For men, we observed a substantial variation across samples in the magnitude of association
between perceived masculinity and attractiveness. The results therefore do not support unequivocal
conclusions that either masculine (Foo, Simmons, et al., 2017; Skrinda et al., 2014) or relatively
more feminine (Perrett et al., 1998; Rhodes et al., 2000) facial traits are universally preferred in
male faces. Perceived masculinity was considered attractive only in two of the samples, the Czech
and the Colombian one. In the Iranian and Turkish sample, the association was also positive but
not statistically significant.
Preference for masculinity might be the result of different adaptive processes in Czech and
Colombian society but it is also possible that the results converge owing to similarities between our
Czech and Colombian sample (not the whole populations). In two studies, Borras-Guevara et al.
(2017a,b) found a negative association between Colombian women’s masculinity preference in
manipulated male faces and Colombian women’s fear of domestic violence. Importantly, raters in
these studies represented various social strata of the Colombian society. Our targets and raters, on
the other hand, were for the most part university students from Bogota. It is thus likely that they repre-
sented a relatively affluent social group where preference for masculine male partners is not
14 Vojtěch Fiala et al.
Table 3. Outline of predictions (a) and significant results (b)
Variable ↓Women Men
Feminine faces perceived more attractive Masculine faces perceived more attractive
Female-typical SShD perceived as more
feminine and attractive
Male-typical SShD perceived as more
masculine and attractive
More average facial configurations
perceived as more attractive
More average facial configurations perceived
as more attractive
Lighter faces perceived as more attractive
Darker faces perceived as more masculine
Wider faces perceived as less feminine Wider faces perceived as more masculine
(Geniole et al. 2015)
Heavier stimuli perceived as less attractive Heavier stimuli perceived as less attractive
Younger stimuli perceived as more
attractive and feminine
Younger stimuli perceived as more attractive
(b) Results based on path analyses, with non-significant trends with p-value [>0.05, <0.1] omitted
Variable ↓Women (Attr|Fem)
Femininity perceived as
more attractive across
5|NA Masculinity perceived as
more attractive in two
perceived as more
attractive in one, more
feminine in two
1|2 Male-typical SShD perceived
as more masculine in
More average faces
perceived as more
feminine and attractive
in two samples
2|2 More average faces perceived
as more attractive in two
Lighter women perceived
as more attractive and
feminine in one sample
1|1 Darker men perceived as
more masculine in three
No effect 0|0 Non-significant trends 0|0
Lower BMI perceived as
more attractive in two
feminine in one sample
2|1 Higher BMI perceived as more
masculine in one sample,
less attractive in one
perceived as more
feminine in one sample
0|1 Younger men perceived as
more attractive in one,
older men perceived as
more masculine in three
Predictions within a given sample (e.g. darker/lighter within an Iranian male sample).
Predictions and results based on age and BMI are not further discussed because they go beyond the scope of the current study. For a more
detailed review and anticipated effects of variables which were not discussed in the Introduction, see Sections S1 and S2.5 in Supplementary
Significant result for Attractiveness|Perceived Sex-typicality; Nout of five female and five male samples, from four samples for each sex
Evolutionary Human Sciences 15
counteracted by fear of domestic violence. The issue of within-populations variance of preferences
could, however, be addressed only in a study that would measure and compare women’s relative safety
from male violence, wealth distribution, and the perception of these environmental factors in both the
Czech and Colombian societies.
Women’s preferences in Middle Eastern countries in our sample (Iran and Turkey) were predicted
neither by sexual shape dimorphism nor by perceived sex-typicality. Both Iranian and Turkish female
raters preferred morphological averageness of facial shape in the male stimuli.
Future studies should explore this observed absence of preference for sex-typicality in Middle East
countries with regard to locally important cultural factors. In particular, it should be taken into account
that in Middle Eastern countries, the tradition of arranged marriages is still widespread, as is women’s
subordinate social role and strong interpersonal bonds (Cindoglu et al., 2011;Friedlandetal.,2016).
Parents and other relatives who negotiate and arrange a marriage may not perceive sex-typicality as highly
important for their choice. Nonetheless, recent evidence shows that people in both countries choose their
partners substantially more freely than in the past (Hart, 2007; Honarvar et al., 2016;Atari&Jamali,
2016a,b), which might imply that the preference for average facial configurations displayed by our
local raters could mirror local adaptations in actual mate choice, unbiased by arranged marriages.
4.2. Association between shape variables and perceived characteristics
Previous studies revealed a weak to moderate association between perceived and morphological sexual
dimorphism (Komori et al., 2011; Mitteroecker et al., 2015). In the current study, neither perceived
femininity nor perceived masculinity and attractiveness were associated with sexual shape dimorphism
(morphological sexual dimorphism) universally across all samples. When restricted only to significant
effects in path analyses, more female-like shape indicated more attractive ratings only in one of the five
women’s samples (Iranian). In two populations (Colombian and Iranian), women with more female-
like facial shape were also perceived as more feminine. In men, a more male-like facial configuration
was predictive of higher perceived masculinity in three of the five samples (Cameroonian, Colombian,
and Turkish). All in all, we thus found no universal association between perceived and measured sex-
typicality in either sex, although in some of the samples, the association ran in the predicted direction.
Facial morphological averageness did not predict perceived characteristics consistently either. In
three of the women’s samples (Czech, Iranian and Turkish) and one men’s sample (Turkish), more
average facial configurations were perceived as more attractive. In the Czech and Turkish women sam-
ple, more average facial configurations were perceived as more feminine. As suggested by current
research, it is possible that facial averageness is a trait that is relatively less important for the perception
of facial characteristics than previously thought (Foo, Simmons, et al., 2017; Holzleitner et al., 2019;
Jones & Jaeger, 2019). It seems, therefore, that morphological variables beyond actual dimorphism
are not good predictors of the perceived facial attractiveness and perceived sex-typicality.
Measured morphometric variables used in this study express facial shape variance as a single num-
ber. Human perception is not, however, a computational device that processes shape in that way. As
suggested by plastic surgery, cosmetics, and related fields, some parameters of facial shape are more
important for perceived attractiveness than others. Such features include relative lip size, lower face
size (Penna et al., 2015) and eyebrow size and shape (Schreiber et al., 2005). Moreover, preferred traits
may be a combination of mature, neotenous and expressive traits (Borelli & Bernerburg, 2010;
Cunningham et al., 1990), not just juvenile/submissive traits (in women) and mature/dominant traits
(in men). Taken together, it is unlikely that the complex phenomenon of human facial trait assessment
could be fully captured by a single morphometric measure.
4.3. Sexual dimorphism in the fWHR
The fWHR was positively associated with sexual shape dimorphism in Cameroonian and Turkish faces
of both sexes and in Czech female and Iranian male faces. Such positive association implies that
16 Vojtěch Fiala et al.
Cameroonian and Turkish men and women with more masculine facial configurations had relatively
wider faces. Past studies identified a slight sexual dimorphism in fWHR (with men having relatively
wider faces; Carré & McCormick, 2008), but a more recent study cast doubt on sexual dimorphism in
fWHR when it ran analyses that controlled for sexual dimorphism in body size (Kramer, 2015).
Özener (2012) found no sexual dimorphism in fWHR in a Turkish university student sample.
Although fWHR is no longer considered a sexually dimorphic measure, our data suggest that
fWHR is associated with more male sex-typical facial shape at least in some of our samples. Except
for the sample of Czech women, fWHR was also significantly positively associated with BMI in all
samples for which BMI was available. In short, it thus turned out that relatively heavier people also
have wider faces regardless of their sex.
4.4. Association between skin lightness and perceived characteristics
We further predicted that skin lightness, which has been associated with perceived sex-typicality and
healthiness in previous research (Carrito & Semin, 2019; Stephen & Perrett, 2016), should be positively
related to perceived attractiveness and sex-typicality (with darker men being perceived as more mascu-
line) in all the five population samples. In the samples of Cameroonian, Colombian and Iranian men,
faces with a darker skin were perceived as more masculine. In the Czech and Turkish men’s sample,
facial skin lightness was not associated with perceived masculinity but the statistically non-significant
associations for both Czech (β=−0.15) and Turkish (β=−0.13) men were in the same direction as
in the rest of the samples. Owing to a low statistical power of our study, we cannot decide whether
this association is ecologically irrelevant in these two countries or whether there indeed exists a stable
association between darker facial skin and perceived sex-typicality across populations.
Regarding women, skin lightness was significantly associated with perceived facial characteristics
only in the Cameroonian female sample. Raters perceived Cameroonian women with a lighter com-
plexion as being both more feminine (perceived femininity) and more attractive. This observation is in
line with previous studies which identified facial skin colouration as a more important cue to the per-
ception of facial attributes in African than in non-African populations (Coetzee et al., 2014; Kleisner
et al., 2017; Strom et al., 2012).
4.5. Limitations of the study
The aim of the current study was to explore the association between perceived and measured facial
traits in samples from five distant countries. To acquire comparable data and results from each inves-
tigated country, we tried to keep the methodology of data collection as similar as possible. Despite
these efforts, we did not manage to maintain all photo acquisition parameters identical throughout
(e.g. camera sensor size or camera-to-subject distance), nor were we able to secure identical lighting
conditions in all samples. Within each sample, however, photo acquisition standards were identical
The questionnaire for Colombian raters differed in the granularity of the rating scale: in Colombia,
the scale ranged from 1.0 to 10.0 with one decimal place, while elsewhere raters responded using
seven-point Likert scales. On the other hand, we analysed all data within-sample rather than aggregat-
ing them across the samples and report standardised coefficients. There is, therefore, no reason to sus-
pect that scale variability affected our results.
Electronic devices used by raters may have caused some differences in ratings owing to, for
instance, differences in the screen size (Třebický et al., 2018). Future studies should either control
for the type of electronic device used or conduct rating sessions under controlled laboratory
On an individual level, preference for sexual dimorphism in men’s faces may have been influenced
by current fertility status and hormonal regulation (Jones et al., 2019 and citations therein) or raters’
relationship status (Little et al., 2002,2007). Unfortunately, we did not collect these data about
Evolutionary Human Sciences 17
participating raters. Concerning other factors on a subpopulation scale, in the Turkish and
Cameroonian samples we explored whether some raters’attributes and some stimuli affected the rat-
ings and significant paths (e.g. the method of skin lightness measurement, combining and separating
subsamples, raters’sex; see Table S4 and Figure S5 in the Supplementary Material). Unfortunately, in
the Cameroonian, Czech and Iranian sample, only tens of individual ratings were available: the subsets
they yielded are thus unlikely to form a reliable base for a comparison of stability of perception across
various social and ethnic groups within the sampled populations.
Although we measured and controlled for several variables (age, BMI, fWHR) and checked for
some aspects of raters’identity, we certainly did not address all possible confounding variables.
Other facial attributes, such as skin colouration with respect to redness and yellowness (Carrito
et al., 2016), contrast between facial features and skin (Stephen & McKeeganh, 2010), or facial hair
(Clarkson et al., 2020) might likewise affect perception of sex-typicality and attractiveness. Future stud-
ies should also account for cross-cultural differences in characteristics that people value in potential
mates, local beliefs and customs, and for the prevailing type of spousal choice.
Current studies on the association between facial attractiveness and sexual dimorphism frequently use
a combination of manipulated stimuli and a forced-choice paradigm that dichotomises participants’
decision-making. It is therefore appropriate to investigate whether conclusions yielded by these setups
can be replicated using different methods. Our results, which were based on non-manipulated facial
photographs, provide limited support to the hypothesis that raters prefer sex-typical features across
populations from distant countries. While perceived femininity of women was preferred in all popu-
lation samples included in our study and the strength of perceived attractiveness–femininity associ-
ation was relatively stable, thus suggesting a potential universality of the association, men’s
perceived facial masculinity was preferred only in the Czech and Colombian sample, that is, in two
distant populations. Our raters from urbanised Iranian and Turkish populations did not prefer facial
masculinity in men and the same applied to raters from the relatively more rural Cameroonian society.
Our study therefore points to a population-specific association of perceived male sex-typicality and
attractiveness based on natural facial stimuli of both sexes.
Further, we showed that morphometric variables (sexual shape dimorphism and facial averageness)
and measured skin lightness were only moderate and inconsistent predictors of perceived sex-
typicality. Presumably owing to this weak predictive power, these variables also did not predict per-
ceived attractiveness across the samples. Measured and perceived sex-typicality tell a different story
with respect to human preference. Ideally, different terms should be applied to measured and per-
ceived traits associated with sex-typicality and averageness and researchers ought to bear in mind
that some traits (e.g. skin lightness) may be population-specific predictors of perceived attributes,
in this case sex-typicality.
Acknowledgements. We wish to thank Oscar R. Sánchez, Eugenio Valderrama and Andrés Castellanos-Chacón for their
help with data collection in Colombia. We also thank TomášKočnar for helping with data collection in Cameroon. We
are grateful to Anna Pilátová for proofreading of the manuscript.
Authors’contributions. KK, VF and PT developed the experimental design of the study. VT, JDL, FP, SAS, RMA and KK
collected the facial stimuli and took the facial measurements. VF, JDL, FP, SAS, RMA and KK collected stimuli ratings. PT
and KK developed the statistical analyses. VF conducted the statistical analyses and drew diagrams. VF, VT and KK wrote the
initial draft, and VF, VT, JDL, PT, FP, SAS, RMA and KK critically revised the manuscript.
Financial support. This study was supported by the Czech Science Foundation (grant number 21-10527S) and by the
Charles University, project GA UK no. 1169120. Data collection in Colombia was supported by Universidad El Bosque,
Vice-rectory of Research (grant number PCI.2016-8835).
Conflicts of interests. The authors declare no conflicts of interest.
18 Vojtěch Fiala et al.
Data availability statement. Original data and other supplementary materials to this article are available online at https://
Ethical statement. The authors assert that all procedures contributing to this work comply with ethical standards of the
relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as
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Cite this article: Fiala V, Třebický V, Pazhoohi F, Leongómez JD, Tureček P, Saribay SA, Akoko RM, Kleisner K (2021).
Facial attractiveness and preference of sexual dimorphism: A comparison across five populations. Evolutionary Human
Sciences 3, e38, 1–24. https://doi.org/10.1017/ehs.2021.33
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