Does Masculinity Matter? The Contribution of Masculine
Face Shape to Male Attractiveness in Humans
Isabel M. L. Scott1, Nicholas Pound2, Ian D. Stephen1, Andrew P. Clark2, Ian S. Penton-Voak1*
1 School of Experimental Psychology, University of Bristol, Bristol, United Kingdom, 2 Department of Psychology, Brunel University, Uxbridge, United Kingdom
Abstract
Background: In many animals, exaggerated sex-typical male traits are preferred by females, and may be a signal of both
past and current disease resistance. The proposal that the same is true in humans – i.e., that masculine men are
immunocompetent and attractive – underpins a large literature on facial masculinity preferences. Recently, theoretical
models have suggested that current condition may be a better index of mate value than past immunocompetence. This is
particularly likely in populations where pathogenic fluctuation is fast relative to host life history. As life history is slow in
humans, there is reason to expect that, among humans, condition-dependent traits might contribute more to attractiveness
than relatively stable traits such as masculinity. To date, however, there has been little rigorous assessment of whether, in
the presence of variation in other cues, masculinity predicts attractiveness or not.
Methodology/Principal Findings: The relationship between masculinity and attractiveness was assessed in two samples of
male faces. Most previous research has assessed masculinity either with subjective ratings or with simple anatomical
measures. Here, we used geometric morphometric techniques to assess facial masculinity, generating a morphological
masculinity measure based on a discriminant function that correctly classified .96% faces as male or female. When assessed
using this measure, there was no relationship between morphological masculinity and rated attractiveness. In contrast, skin
colour – a fluctuating, condition-dependent cue – was a significant predictor of attractiveness.
Conclusions/Significance: These findings suggest that facial morphological masculinity may contribute less to men’s
attractiveness than previously assumed. Our results are consistent with the hypothesis that current condition is more
relevant to male mate value than past disease resistance, and hence that temporally fluctuating traits (such as colour)
contribute more to male attractiveness than stable cues of sexual dimorphism.
Citation: Scott IML, Pound N, Stephen ID, Clark AP, Penton-Voak IS (2010) Does Masculinity Matter? The Contribution of Masculine Face Shape to Male
Attractiveness in Humans. PLoS ONE 5(10): e13585. doi:10.1371/journal.pone.0013585
Editor: Virginia J. Vitzthum, Indiana University, United States of America
Received July 16, 2010; Accepted September 20, 2010; Published October 27, 2010
Copyright: ß 2010 Scott et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by the University of Bristol. IDS is supported by the Economic and Social Research Council, UK. The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: I.S.Penton-Voak@bristol.ac.uk
association between either testosterone or masculinity and disease
resistance is scant, inconsistent, and largely negative [7,12–20].
Even if masculinity does signal past disease resistance, it is unclear
that females will, in general, benefit from attending to this signal,
particularly if cues to current condition are available. Past disease
resistance may be a weak predictor of current and future
resistance, especially if pathogenic complexity is high, and
pathogen fluctuation is fast (relative to host lifespan and generation
length) [21,22]. Recent mathematical models of mate choice
suggest that in most environments, females can reliably derive
substantial fitness advantages from attending to current condition,
but may gain little, if any, further benefit from simultaneously
selecting mates on the basis of past immune function [21,22].
Thus, stable traits such as masculinity, which are not influenced by
short-term fluctuations in adult health, should be of less
importance to attractiveness than other more condition-responsive
cues. This expectation is stronger in animals with long lifespans
and slow reproduction, such as humans.
Consistent with this reasoning, findings relating male attractiveness to long-term health and/or stable facial traits have to
date been equivocal [14,23–27]. In particular, reported mascu-
Introduction
Many researchers studying non-human mate choice have
observed that exaggerated sex-typical male traits, such as large
antlers and peacock’s tails, are attractive to females [1]. Authors
have suggested that the growth of such traits is mediated by
immune-stressing steroids such as testosterone, and that as only
high quality males can ‘‘afford’’ exposure to immune stress, these
traits signal high levels of immunocompetence [2–7]. Such
perspectives have generated similar expectations regarding human
mate choice – i.e. that masculine males should be attractive, and
that this attractiveness is attributable to immunocompetence [8].
These proposals form the basis of a large literature on human
preferences for facial masculinity [9].
More recently however, a number of authors have questioned
immunocompetence perspectives on facial masculinity preferences. Recent reviews of the animal literature present a complex and
uncertain picture of the relationship between immunity, testosterone and trait size [7,10]. In humans, preliminary evidence suggests
there is an association between circulating testosterone levels and
anatomical masculinity in faces [11], but the evidence for an
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Does Masculinity Matter?
linity preferences have been highly inconsistent across participants and methodologies – in stark contrast to men’s reliable
preferences for facial femininity in women [9,20,28–30]. In spite
of this, and of the large volume of literature on masculinity
preferences, little attempt has been made to quantify the
contribution of naturally-occurring variations in facial masculinity to ‘‘real life’’ attractiveness. Many studies to date have
employed computer-based morphing methods to increase or
decrease the masculinity of a particular facial photograph, and
thereby measure the influence of masculinity on preferences. As
such methods eliminate variation in other, potentially competing
cues to attractiveness, they force participants (often in a forcedchoice paradigm) to attend to masculinity alone, and cannot be
used to gauge its importance in realistic contexts. While
correlational approaches using unmodified photographs of
individuals should address this concern, experiments to date
have largely relied on subjective measures (i.e. ratings) of facial
masculinity as independent variables [18,31–34]. Few studies
have attempted objective assessments of anatomical masculinity
in faces, and those that have done so have used relatively simple
measures and/or produced inconclusive results [14,27,31,35].Using the best measure (of which we are aware) of facial masculinity
to date, researchers found no evidence of a relationship between
masculinity and attractiveness, although this measure correctly
classified only 75% of faces by sex [14]. The importance of facial
sexual dimorphism as a component of attractiveness is therefore,
surprisingly, currently unknown.
In contrast to the large body of literature regarding the role of
stable traits in human mate choice [9], research on condition and
attractiveness has been limited. In spite of this, empirical evidence
is broadly consistent with the view that men’s current health
influences attractiveness, [9,34,36–38,although see 39], and a
number of cues have been identified via which this influence may
be achieved. Skin cues such as overall skin colour and colour
homogeneity, for example, are observable, objectively measurable,
and known correlates of condition in humans and non-human
animals [38,40,41]. Colour information influences judgments of
attractiveness [40,42], health [41–43] and facial identity [44], and
may contribute more to sex-discrimination than does shape
information [45,46]. Research on attractiveness and skin colour
is a relatively recent phenomenon however, and as with
masculinity research, has largely relied on subjective measures or
morphing techniques [38,41]. Those studies that have used
objective measures of natural variation in skin colour, and tested
whether they predict attractiveness in the presence of variation in
competing cues, have been limited to female faces [40,42]. It is
unclear, therefore, whether skin colour is an important component
of male attractiveness.
To explore these issues, we measured associations between
sexual dimorphism and attractiveness in male faces. In two
independent samples, geometric morphometric analysis of the
configuration of a large number of facial landmarks was used to
generate an objective measure of natural variation in morphological masculinity, and the extent to which it predicted attractiveness
was assessed. To further investigate the relative contribution of
stable versus condition-dependent cues, we extracted facial skin
colour information from the faces. This information was entered
into a regression model along with morphometric masculinity to
determine the extent to which either one could predict
attractiveness.
Participants
Subjects participated in the ratings experiment in exchange for
course credit or cash payment.
Sample 1. Twenty-two female undergraduate students (age
range 18–21, mean 19.5, SD .66), recruited via University of
Bristol.
Sample 2. Forty-nine students and members of staff from
Bristol University. Eighteen [10 women, 8 men, age range 19–41,
mean age 27, SD 7.3] viewed whole faces. Thirty-one (20 women,
11 men, age range 18–70, mean age 31, SD 11) viewed skin
patches only.
Stimuli
Two sets of colour facial photographs of Caucasian males who
were facing forward, and told to adopt a neutral , relaxed
expression were employed in this study.
Sample 1. Twenty photos collected from a community
sample of men from northern England (mean age 27, SD = 3).
Participants were photographed sitting, 1.5 metres from a digital
camera (Nikon E950) in front of a black background. Subjects
were illuminated with fluorescent light with no flash.
Sample 2. Seventy-five photos collected from students at
Stirling University (mean age 21, SD = 2). Skin patch stimuli were
also generated from these photos (section 2.3.2). Participants were
standing (ensuring replicable natural head position), 1.5 metres
from the digital camera (Canon PowerShot G1), in front of a grey
background. Subjects were lit with bilateral studio lights (slightly
offset to provide some depth information), in a room with no
natural light. No flash was used.
Measures
Morphometric masculinity: Sample 1. The 20 male faces
were part of a larger photoset of 62 male and female faces from the
same population of adults. A geometric morphometric analysis of
all of these faces was used to generate morphological masculinity
scores for each face in a manner analogous to that use used for
previously for bodies [47]. First, using criteria established by
Stephan et al [48], the x-y coordinates of 129 facial landmarks
(Fig. S1 – supplementary material) were delineated for each face
using Psychomorph [49]. Geometric morphometric techniques
were then used to calculate a masculinity index for each face.
Morphologika [50] was used to carry out Procrustes registration of
the landmark data - a best fit procedure that removes scale,
rotational and translational differences between shapes [51–53].
Next, to identify dimensions of variation in facial landmark
configuration, Morphologika was used to conduct Principle
Components Analysis (PCA) of the Procrustes-registered landmark
data. A Kaiser-Guttman criterion was used to select Principle
Components (PCs) for inclusion in subsequent analysis; i.e. those
with eigenvalues greater than the average eigenvalue were
retained. This led to the retention of the first 11 PCs which
together accounted for 84.7% of the variance in facial landmark
configuration (see Table S1, supplementary material for details).
Step-wise discriminant analysis (SPSS 13) was then used to
establish which of the 11 PCs were best able to discriminate
between the male and female faces. The resulting discriminant
function incorporated eight of the PCs (Wilks’ l = 0.163; df = 8;
x2 = 101.6, p,0.00001), and yielded correct sex classifications for
96.8% of faces (see Table S1, and Fig. S2, supplementary material,
for details). Discriminant function scores were therefore used as an
index of morphological masculinity, with high scores indicating a
more masculine facial structure (see Table S1, supplementary
material for details).
Methods
Experiments were conducted using two photo-samples.
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Morphometric masculinity: Sample 2. Morphological
masculinity was calculated in the same manner as sample 1,
using a set of 150 faces (75 male, 75 female) from the same
population, and with discriminant function scores again being
used as an index of facial masculinity (with high scores indicating a
more masculine facial structure; Fig. 1 a) for examples). Twentyone PCs were retained from the PCA, accounting for 85.7% of the
variance in facial landmark configuration (Table S2). Step-wise
discriminant analysis determined that 11 PCs were best able to
discriminate between the male and female faces. The resulting
discriminant function was again a powerful discriminator (Wilks’
l = 0.134; df = 11; x2 = 286.6, p,0.00001), yielding correct sex
classifications for 98.7% of participants (Table S2, Fig. S3).
Skin colour. For each face from sample 2, the average
lightness, redness and yellowness, as defined by the CIELab color
space, was calculated across pixels using Matlab (see Fig. 2, for
examples). The CIELab color space is defined by L* (lightness), a*
(redness) and b* (yellowness) color dimensions, and is modelled on
the human visual system. It is designed to be perceptually uniform,
with a change of one unit appearing to be of approximately the
same magnitude regardless of its dimension [54]. Increases in
facial skin L*, a* and b* values enhance apparent health[41,43]
and therefore affect attractiveness in human faces [38].
In order to check whether skin appearance itself – and not just
some morphological correlate of skin appearance – contributes to
attractiveness, we also collected ratings of skin health using stimuli
that did not display shape information. To create the stimuli,
patches of 114*142 pixels were extracted from both left and right
cheeks of the faces from sample 2, with the inner top corner of
each patch positioned at a fixed height vertically below the pupil.
The resulting skin patch stimuli displayed colour but not shape
information (see supplementary material, Fig. S4, for examples),
and were used to examine the relationship between objective
colour cues, perceptions of skin health, and whole face
attractiveness. To this end, skin patches were rated for apparent
health by 31 independent participants, following prior methods
[38]. Each image was enlarged by 100% and then presented to
participants in a random order on a computer screen, who rated
them for health on a scale of 1 to 7. Inter-rater reliabilities were
high (Cronbach’s alpha = .950 (left patches), .963 (right), .954
(both sides together)). For each face, ratings were averaged for left
and right patches across all participants to create an overall skin
health score.
Rated attractiveness: Sample 1. Subjects viewed a
computer presentation of the 20 male photos, in random order,
and rated each of them for attractiveness on a scale of 0–9. Each
photograph was assigned a score for rated attractiveness by
averaging responses across participants. Inter-rater reliability was
high (Cronbach’s a = .894).
Rated attractiveness: Sample 2. Photographs were rated in
the same manner as sample 1, using a scale of 1–7. Inter-rater
reliability was high (Cronbach’s a = .932). To determine whether
any patterns were specific to male faces, the 75 female faces were
also rated for attractiveness. Inter-rater reliability was again high
(Cronbach’s a = .897).
the third quartile or lower than the first quartile. Outliers are
excluded from the relevant subsequent analyses, although
including them did not affect the significance of results.
Morphological masculinity and attractiveness
Linear regressions were conducted, with faces as subjects,
morphological masculinity as the independent variable and mean
attractiveness rating for each face as the dependent. There was no
significant linear relationship between these two variables in either
set of male photographs (sample 1: F(1,19) = 1.134, beta = .243,
t = 1.065, p = .301, sample 2: F(1,73) = 1.108, beta = .123,
t = 1.053, p = .296). Including further nonlinear terms indicated
likewise that the relationship between morphological masculinity
and attractiveness was not significantly approximated by a
quadratic or logarithmic model (sample 1: both F,1.36,
p..259, sample 2: both F,1.024, p..315).
To validate our measure of morphological masculinity, analyses
were repeated for the female faces from sample 2 (see section
2.3.2). In contrast to the male faces, the relationship between
masculinity and attractiveness in female faces was significant and
negative (F(1,72{) = 6.339, beta = 2.286, t = 2.518, p = .014, {two
outliers were excluded from analyses), implying that the most
feminine (i.e. least masculine) females were attractive, and that our
measures would have detected a similar effect in male faces had it
existed.
To exclude the possibility that our null findings were due to
individual variation in female preferences cancelling each other
out (as a result of menstrual cycle phase, own attractiveness or any
other individual difference variable that has been shown to
influence preferences for masculinity in face shape), additional
correlations between morphological masculinity and attractiveness
ratings were performed for each participant individually. In
sample 1, significant correlations between morphological masculinity and attractiveness ratings were observed among two of the
participants (both correlations positive, p,.05), and in sample 2
there were no significant correlations (all p..05). Thus, among the
large majority (95%) of raters, there was no evidence of preference
for masculinity, either positive or negative, a finding consistent
with a generalised indifference to masculinity as a cue of mate
value.
Skin colour and attractiveness
To investigate the effect of skin colour, a further (backward)
linear regression was conducted on the faces from sample 2, with
attractiveness as a dependent variable, and morphological
masculinity, skin lightness, yellowness, and redness and as
independent variables. There was one significant correlation
among the independent variables (between skin yellowness and
skin lightness (Pearson correlation r(75) = 2.315, p,.01)), but
tolerance testing indicated that standard assumptions regarding
multicolinearity were not violated (all VIF,1.141). The regression
retained only skin yellowness as a predictor of attractiveness, and
the effect of skin yellowness was positive and highly significant
(F(1,71) = 10.806, Beta = .366, t = 3.287, p,.002). Skin lightness,
redness and morphological masculinity did not significantly
predict attractiveness (all p..114, see Table 1).
While skin cues were therefore correlated with attractiveness, it
is feasible that participants’ responses to faces were not actually
influenced by skin appearance, and instead were determined
exclusively by some non-dimorphic shape cue, which was also a
correlate of skin appearance. To explore this possibility, ratings of
skin-patch health were entered into a Pearson correlation with
whole-face attractiveness ratings. Results showed a significant
correlation between rated skin-patch health and whole-face
Results and Discussion
Kolmogorov-Smirnov tests indicated that all measures (attractiveness, morphological masculinity, skin redness, skin yellowness,
skin-patch health ratings) were normally distributed within each
sample of males. Sample 2 contained one outlier for morphological masculinity and two for skin redness; that is, cases with values
that were more than 1.5 times the interquartile range higher than
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Figure 1. Masculinity, measured, rated and morphed: examples of dimensions. To preserve anonymity of participants, these faces are
composites rather than real individuals. a) Morphological masculinity. Examples of faces scoring low (left) and high (right) on this measure. b) Rated
masculinity. Examples of faces rated as low (left) and high (right) masculinity. c) Digitally morphed masculinity. Example of a face morphed in the
feminine (left) and masculine (right) direction.
doi:10.1371/journal.pone.0013585.g001
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Figure 2. Colour variation: examples of stimuli. a) Faces scoring low (left) and high (right) for lightness (L*). b) Faces scoring low (left) and high
(right) for redness (a*). c) Faces scoring low (left) and high (right) for yellowness (b*).
doi:10.1371/journal.pone.0013585.g002
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Table 1. Relationship between objective traits and attractiveness.
Sample 1 (n = 20)
Sample 2 (n = 74{), masculinity
only in model
Sample 2 (n = 72{), masculinity and
colour cues in same model
Beta
p
Beta
p
Association with Attractivenessa
Independent variables
Beta
b
p
Morphological masculinity
.243
.301
.123
.296
.140
.215
Skin lightness
-
-
-
-
2.185
.114
Skin rednessb
-
-
-
-
.011
.921
Skin yellownessb
-
-
-
-
.366
.002**
Result of linear regressions with attractiveness as dependent variable, and morphological masculinity and skin colour as independent variables.
a
As rated by participants.
b
Measured using methods described in section 2.3.
{
Total sample = 75, outliers excluded where appropriate.
**p,.01.
doi:10.1371/journal.pone.0013585.t001
attractiveness (r(75) = .266, p = .021). As the skin patches did not
contain any visual information regarding shape/morphology, this
relationship suggests that responses to faces are influenced (at least
in part) by skin appearance, and not entirely by shape-correlates of
skin colour. It is therefore likely that skin appearance actually
affects attractiveness, rather than merely being associated with it in
virtue of some other third variable.
There was no evidence of a main effect of masculinity on
attractiveness, but masculinity may nevertheless be a ‘‘secondpass’’ predictor of attractiveness. That is, masculinity may be
attractive in faces which exhibit other, more important cues of
attractiveness, and irrelevant in other faces. This would make it
more difficult to detect a main effect of masculinity on
attractiveness. To examine this possibility, we performed a
moderator analysis [55], with skin yellowness treated as a potential
moderator of the influence of masculinity on attractiveness. That
is, a hierarchical multiple regression was performed, with
morphological masculinity and skin yellowness (both z-standardised) entered in step one, and the product of these two variables
entered in step two. Results indicated that only skin yellowness was
a significant predictor of attractiveness in this model (Beta = .379,
t = 3.418, p = .001). Neither masculinity nor the interaction
between masculinity and skin yellowness were significant predictors of attractiveness (both Beta,.15, p.217). There was therefore
no evidence in our data that masculinity was either a first or a
second-pass criterion of attractiveness.
based on judgements of face shape alone, and the term
‘‘masculinity’’ is liable to being interpreted as normative, and
therefore to imply health and/or attractiveness. Consistent with
this proposal, prior authors have found that rated masculinity is
correlated with perceived health, and that this may explain part of
the attractiveness of masculine-rated faces [34]. Associations
between rated masculinity and attractiveness may not, therefore,
imply a relationship between objective shape-masculinity and
attractiveness.
Morphing methods often use objective criteria of masculinity,
but are subject to the alternative shortcoming that only one
variable is manipulated in the construction of the stimuli, and
choice based on other variation in facial appearance is eliminated.
As a result, such experiments offer little information about the
contribution of masculinity to attractiveness in real faces, where
other, potentially more salient traits will also vary, and may eclipse
any variation in masculinity. Indeed, recent research suggests that
other shape traits which influence responses in morphing
experiments may explain very little variation in the attractiveness
of real faces [26,56]. Moreover, so long as the female participants
share a common, systematic method of ranking the faces viewed in
such experiments, test responses will be non-random – even if
participants are indifferent to masculinity. Systematic biases in tests for
masculinity preferences might, for example, be an epiphenomenon
of preferences for averageness; averageness preferences are widely
documented, and masculinising a face makes it either more or less
like the male average, depending on its starting level of
masculinity. Even if, as our above findings suggest, participants
are indifferent to masculinity in ‘‘real life’’, the application of
‘‘averageness’’ preferences to the faces used in masculinity
research could generate systematic responses to particular faces,
depending on their starting level of masculinity. Such a
phenomenon would explain why morphing methods report
directionally inconsistent effects of masculinity on attractiveness,
with masculinised faces looking better approximately half of the
time [57,58].
Both of the more commonly used prior methods for assessing
preferences may therefore elicit significant biases regarding
masculinity with a frequency that offers apparent – but perhaps
misleading – confirmation of its importance. To explore this
possibility, we conducted a further study in which we re-measured
masculinity preferences with both of these methods. That is, a) by
assessing the relationship between a subjective measure of perceived
(i.e. rated) masculinity and attractiveness, and b) by morphing each
The influence of testing methodology
Our results suggest that women are indifferent to morphological
masculinity when viewing unmanipulated faces of individual men.
A number of previous authors have reported that women do
respond to male facial masculinity when making judgments about
attractiveness. However, this apparent contradiction may be
attributable to some important methodological differences between the present study and previous work.
As outlined earlier, most previous research in this area has
depended on either examining associations between attractiveness
ratings and subjective measures of perceived masculinity for
individual faces; or using morphing techniques to create stimuli
based on male-female differences in face shape for attractiveness
rating/choice tasks. The subjective approach has generally found a
small, but consistently positive association between perceived
masculinity and attractiveness. However, the use of subjective
measures is problematic; ratings of masculinity are unlikely to be
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examples). To investigate whether the relationship between rated
masculinity and attractiveness is mediated by perceptions of
health, ratings of apparent health were also collected. Inter-rater
reliabilities were high for both variables (Sample 1: Cronbach’s
a = .902 (health), .829 (masculinity); sample 2: = .904 (health),
.880 (masculinity)).
individual face to create a masculinised and a feminized version,
then performing a two-alternative forced-choice (2AFC) preference test similar to those used in previous studies [59]. We used the
same two sets of faces as in our previous experiment (section 2.2),
for which we had already established that natural variation in
morphological masculinity did not predict attractiveness. Based on
prior results, we hypothesised that using these two prior methods
would yield: a) a weak, positive correlation between rated
masculinity and attractiveness, and b) a strong but directionally
inconsistent effect of morphed masculinity on attractiveness, with
feminine faces looking better masculinised and vice versa. Results
such as these would show that prior methods produce statistically
significant results even when morphological masculinity does not really
predict attractiveness. If so, the use of such methods may have
generated an inflated impression of the extent to which
masculinity is a cue of attractiveness.
Preference for digitally masculinised versus feminised
faces
Sample 1. Masculinised and feminised versions of the
photographs were created using established software-based
morphing techniques [29,60]. That is: 168 x-y coordinates were
used to define a male-female vector, determined by average
differences in the position of the landmarks between male and
female faces. The original 20 male images were transformed in
shape along this vector, with each face transformed 80% in both
masculine and feminine directions. This generated 20 pairs of
masculinised and feminised male faces (see fig. 1 c).
Independent subjects then viewed these pairs of masculinised/
feminised faces, in random order. Participants stated which of the
two faces looked more attractive, on a scale of 1 to 8, following
previous research [60]. The proportion of trials in which the
masculinized face had been chosen was calculated for each face, to
give an indication of how much a given face looked better
masculinized.
Sample 2. Masculinised and feminised versions of the
photographs were created using the same methods as in study 1,
but with a 50% morph. The procedure was the same as for sample
1, and participant preferences were calculated for each face as
before.
Methods
All participants completed an informed consent form having
been given written and verbal details of the tasks to be completed.
This work was approved by the Faculty of Science Human
Research Ethics Committee of the University of Bristol.
Participants
Sample 1. One hundred and sixty-two women, recruited via
University of Bristol. Twenty-two (age range 18–21, mean 19.5,
SD .66), rated unmodified faces for attributes. A further 140 (age
range 18–57, mean 20.33, SD 5.51) took part in the digital-morph
forced-choice test (section 4.2.4).
Sample 2. One hundred and forty-eight students from Bristol
and Stirling Universities. Eighteen (age range 19–41, mean 27, SD
7.34) rated faces on the attributes. A further 110 women (age
range 18–70 mean 20.65, SD 4.94) performed the digital-morph
forced-choice test.
Results
Rated masculinity approach
In contrast to the results in section 3.1, there was a significant
positive correlation between rated masculinity and attractiveness in
both samples when using subjective measures (Pearson correlations,
sample 1: r(20) = .513, p = .021, sample 2: r(75) = .248, p = .032, see
table 2). This finding indicates that subjective judgments of
masculinity are based on factors other than just morphological
masculinity. Consistent with this, tests indicated that in both
samples, rated masculinity was correlated with rated health
(sample1: r(20) = .650, p = .002, sample 2: r(75) = .259 p,.025),
and when rated health and rated masculinity were both entered into
a regression as predictors of attractiveness, only rated health was a
significant predictor of attractiveness (sample 1; health: Beta = .962,
Stimuli
Original, unmodified photos from study 1 (see section 2.2) were
used to test the influence of rated masculinity on attractiveness (see
section 4.2.4). Masculinised and feminised versions of these same
photographs were created and used as stimuli for the digital
morphing approach, (see section 4.2.4).
Rated masculinity
Photographs from both samples were rated for masculinity using
an identical procedure to that in section 2.3.3 (see fig. 1 b) for
Table 2. Relationship between subjective traits and attractiveness.
Association with Attractivenessa
Apparent health
a,b
Sample 1 (n = 20)
Sample 2 (n = 75)
.890***
.791***
Apparent masculinitya,b
.513*
.248*
Apparent masculinity, controlling for healthc
2.112ns
.047ns
a
As rated by participants.
Result of Pearson correlation.
c
Beta value when rated health is included in regression along with rated masculinity.
ns
p..10.
*p,.05.
**p,.01.
***p,.001.
doi:10.1371/journal.pone.0013585.t002
b
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Does Masculinity Matter?
facial preferences [9], no rigorous attempt has been made to affirm
that masculinity matters when competing cues of health are
available. In contradiction to the general assumption that
masculinity does matter, our data provided no evidence of any
systematic relationship, either linear or nonlinear, between
masculinity and attractiveness in unmanipulated (i.e. real) faces.
Instead, we find that yellow skin colour is a highly significant
predictor of preferences, and explains more variation in
attractiveness than either rated or measured masculinity. These
results suggest that, for these samples at least, masculinity is not a
primary determinant of male attractiveness.
There are a number of reasons to suppose that these findings
are robust. Our stimuli depicted a wide range of faces, as the only
dimension on which either sample was deliberately restricted was
age (both samples depicted males of an appropriate age to be
considered as possible mates by the participants). Indifference to
masculinity was apparent in both group-level and individual level
analyses, and patterns in perceptions were consistent across the
two samples (tables 1, 2), with different pools of female raters, and
with stimuli drawn from populations differing in geographic
location and socio-economic status, and photographed under
different lighting conditions. Our objective measure of masculinity
was based on information from multiple feature-points, was a
powerful discriminator between the sexes, and a significant
predictor of attractiveness in female faces. If masculinity were a
comparatively strong predictor of attractiveness in either sample,
we would therefore expect to have detected this in our tests.
The indifference to morphological masculinity seen among our
participants has not generally been apparent in prior research
[although see 14, and 62 for similar findings relating to preference
shifts]. Our proposed explanation for this fact is that two of the most
common methods for measuring preferences produce significantly
nonrandom results even when masculinity doesn’t matter, and that these
results have led to an overestimation of female interest in
masculinity. Consistent with this proposal, we found that rated
masculinity – in contrast to morphological masculinity – was a
positive predictor of attractiveness, but this relationship was
eliminated once health was controlled for. This finding indicates
that participants’ judgments when rating masculinity are based on
additional traits other than morphological masculinity (such as
colour cues, or potentially, semantic associations between masculinity and attractiveness) and hence explains the apparent attractiveness
of masculinity in previous experiments. With regard to digital
morphing methods, a significant majority of faces in both samples
elicited directional preferences for either masculine or feminine
versions, even though there was no relationship between ‘‘natural’’
morphological masculinity and attractiveness in either set of photos.
These results show that preferences which are elicited via digitalmorph forced-choice experiments are, potentially, a poor indicator
of the real world importance of masculine face shape. Prior research
has reported a relationship between responses to caricatured/forcedchoice masculinity preferences tests and actual partner’s masculinity
[59], but the direction of causality is unclear. Our analyses suggest
that responses in such experiments may reflect a general preference
for averageness, rather than a particular interest in masculinity
(although, we note that we did not find the predicted quadratic
relationship between masculinity and attractiveness in experiment 1).
Thus, partner’s masculinity may influence perceptions of what is
average and hence responses to caricatured stimuli, rather than
preferences for masculinity influencing mate choice.
Nevertheless, our results require replication. If there is, in fact,
any effect of masculinity on attractiveness, then this will be easier
to detect in samples where variation in masculinity is high, and
variation in other traits is low. The replication of our experiments
t = 6.723, p,.0005, masculinity: Beta = 2.112, t = 2.783, p = .444;
sample 2; health: Beta = .779, t = 10.463, p,.0005, masculinity:
Beta = .047, t = .625, p = .534). These results indicate that the
impression of a relationship between masculinity and attractiveness,
as reported in prior research using subjective measures of
masculinity, may be an artefact of the way in which the term
‘‘masculinity’’ is interpreted by participants.
Digitally Morphed Masculinity Approach
To determine whether using masculinised and feminized
versions of each face in a forced-choice design would elicit
significant preferences for masculinity or femininity as predicted, a
one-sample t-test was performed, with faces as subjects, on the
proportion of trials for which the masculinised version of each face
was chosen (against the chance value of 0.5). In sample 1, there
was a significant overall bias towards masculinised versions being
chosen (t(19) = 4.871, p,.0005). In sample 2, there was no
significant overall bias in either direction, but this was largely due
to directional preferences for certain faces cancelling each other
out. A face-by-face analysis (binomial tests for deviations from 0.5)
indicated that, in both samples, the majority of faces elicited
significant directional preferences for either masculinity or
femininity. In sample 1, 15 of the 20 (75.0%) faces elicited
statistically significant directional preferences regarding masculinity/femininity, with 14 looking better masculinised. In sample 2,
40 of 75 (53.3%) the faces elicited significant directional
preferences, with 22 looking better masculinised. Binomial tests
indicated that for each sample the proportion of faces eliciting
significant biases was significantly greater (p,.0001) than the 5%
expected by chance if morphing did not tend to produce
directional preferences for some faces; 75.0% for sample 1 (95%
CI: 50.9%–91.3%) and 53.3% for sample 2 (95% CI: 41.5%–
65.0%). These findings show that, by holding all other traits
constant, digital morphing methods can elicit statistically significant preferences in a sample of photographs for which ‘‘real’’
variation in morphological masculinity does not actually predict
attractiveness (as shown in section 3.1).
The existence of significant preferences in forced-choice experiments with morphed faces indicates that women agree on whether a
given face looks better masculinised. As outlined in section 4.1, this
fact is not inconsistent with ‘‘real life’’ indifference to masculinity,
provided that some criteria can be identified (other than masculinity)
via which women rank the test faces for attractiveness. Our results
are somewhat consistent with the hypothesis that averageness is such
a criteria: in the larger sample of photographs, level of morphological
masculinity in the original photo was significantly and inversely
related to the proportion of times that the masculinised version of a
face was preferred by participants (r(75) = 2.260, p,.026), although
this effect was not statistically significant in sample 1 (r(20) = 2.302,
p = .196). That is, naturally masculine faces were more likely to look
better feminised, and vice versa, suggesting that when participants
were forced to discriminate on the basis of masculinity alone,
‘‘averagely’’ masculine faces were chosen. This finding provides an
explanation as to how women can demonstrate a high level of
agreement among themselves regarding whether a face looks better
masculinised or feminised in forced choice experiments, in spite of
being indifferent to masculinity in faces that vary naturally across
multiple dimensions.
Discussion
Most of the literature on facial masculinity preferences has
focused on whether the influence of masculinity on attractiveness is
positive or negative [57,61]. Despite the volume of literature on
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Does Masculinity Matter?
in photosets with a wider range of ages, as well as a limited and/or
attractive range of skin colours would therefore be instructive. In
addition, we note that the individuals from our sample population
have access to modern medicine and nutrition, high exposure to
individuals from outgroups, and experience high levels of gender
equality. Factors such as these may undermine the relationship
between masculinity and immunity [8,63], introduce high levels of
variation in the parameters of competing cues (such as colour,
overall size, or other types of shape variation), or simply reduce
interest in sex-typicality in general [64]. Interest in yellow skin
colour might likewise be the result of some anomalous ecological
or cultural factor, and it would therefore be instructive to attempt
replication of our findings in populations with differing social and
ecological backgrounds. The contrast between our participants’
apparent lack of interest in sex-typicality and preferences observed
in many non-human groups [1], also requires investigation. One
possibility is that women do in fact find sex-typicality attractive,
but rely on other cues such as body masculinity [47] or simpler
cues of the type used in much non-human research, such as overall
size [65]. Another, more speculative hypothesis, is that, following
the logic of Adamo and Spiteri’s model [22], humans are exposed
to unusually fast levels of pathogenic fluctuation relative to life
history, resulting in a reduced emphasis on stable cues of past
disease resistance, and increased emphasis on condition.
In summary, our results suggest that the influence of masculine
face shape on attractiveness may have been overstated in humans.
This finding does not, of course, negate the possibility that
masculinity may be a cue of other socially important traits such as
age, dominance and/or aggression [19,20,29], and may therefore
be of relevance both to personality perception and intrasexual
selection. It does, however, contrast with the significant relationship between colour cues and attractiveness in these faces, and
supports a shift in emphasis in physical attractiveness research
toward the study of such condition-dependent cues.
composite female face. For definitions of landmarks see Stephan
et al (2005).
Found at: doi:10.1371/journal.pone.0013585.s001 (0.65 MB TIF)
Figure S2 Distribution of discriminant function scores for males
and females, sample 1. Stacked histogram showing distribution of
discriminant function scores for males (n = 31) and female (n = 31)
from sample 1. Faces with discriminant scores .0 were classified
as male by the function, those with scores ,0 were classified as
female.
Found at: doi:10.1371/journal.pone.0013585.s002 (0.58 MB TIF)
Figure S3 Distribution of discriminant function scores for males
and females, sample 2. Stacked histogram showing distribution of
discriminant function scores for males (n = 75) and female (n = 75)
from sample 2. Faces with discriminant scores .0 were classified
as male by the function, those with scores ,0 were classified as
female.
Found at: doi:10.1371/journal.pone.0013585.s003 (1.01 MB TIF)
Figure S4 Examples of skin patches from sample 2. a) Patches
scoring low (left) and high (right) for lightness (L*) b) Patches
scoring low (left) and high (right) for redness (a*) c) Patches scoring
low (left) and high (right) for yellowness (b*)
Found at: doi:10.1371/journal.pone.0013585.s004 (0.45 MB TIF)
Principal components for the morphometric analysis
of Sample 1.
Found at: doi:10.1371/journal.pone.0013585.s005 (0.04 MB
DOC)
Table S1
Principal components for the morphometric analysis
of Sample 2.
Found at: doi:10.1371/journal.pone.0013585.s006 (0.04 MB
DOC)
Table S2
Author Contributions
Supporting Information
Conceived and designed the experiments: IMLS ISPV. Performed the
experiments: IMLS. Analyzed the data: IMLS NP IDS ISPV. Contributed
reagents/materials/analysis tools: NP IDS. Wrote the paper: IMLS NP
APC ISPV. Made figures: APC.
Figure S1 The 129 facial landmarks used in the morphometric
analyses of masculinity. Landmarks are represented on a
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