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. Author manuscript; available in PMC: 2022 Mar 1.
Published in final edited form as: Adapt Human Behav Physiol. 2020 Nov 12;7(1):73–88. doi: 10.1007/s40750-020-00156-y

Effects of Male Facial Masculinity on Perceived Attractiveness

Omid Ekrami 1,*, Peter Claes 2,3,4, Mark D Shriver 5, Seth M Weinberg 6,7, Mary L Marazita 6,7, Susan Walsh 8, Stefan Van Dongen 1
PMCID: PMC7872150  NIHMSID: NIHMS1644675  PMID: 33575162

Abstract

Studies suggest that high levels of masculinity in men can be a signal of ‘better genes’ as well as low parental investment. It is the trade-off between these two qualities that has led to the hypothesis that women’s preferences for male masculinity are condition-dependent, yet, not all studies support this hypothesis. In addition, there is evidence that more average faces would be perceived as more attractive. Here we study the variation in masculinity preferences of a cohort of heterosexual women (n=769), using manipulated 3D faces of male subjects. We used linear mixed models to test for effects of various covariates such as relationship status, use of hormonal contraception, sociosexual orientation and self-perceived attractiveness on preference for masculinity. Our results show that women’s sociosexual orientation has a positive correlation with masculinity preference while using hormonal contraception decreases this preference. None of the other covariates displayed any significant effect on masculinity preference. The initial level of masculinity of the faces (very low, low, average, high and very high) was also shown to affect this preference, where we found a significant preference for higher masculinity in the very low and average group, while no preference was found in the other groups. Our findings support the notion that condition-dependent variables have very small effects, if any, on women’s preference for masculinity in men.

Keywords: Masculinity; attractiveness; women’s preferences; forced-choice experiment, condition-dependent

1. Introduction

Several studies have estimated associations between perceived attractiveness of the human face and its sex-typical features (measured on a masculinity-femininity scale) (Little and Hancock 2002; Grammer et al. 2003; Holzleitner et al. 2014). The idea that masculine men (and to some extent feminine women) should appear more attractive is based on the immunocompetence handicap hypothesis (ICHH) (Folstad and Karter 1992). Steroid hormones affect the development of secondary sexual traits, what we know as masculinity in men’s faces, and are displayed through larger jawbones, prominent cheekbones and thinner cheeks compared to women (Little et al. 2011). These hormones may also act as immunosuppressant (Thornhill and Moller 2007). Therefore, individuals who can ‘withstand this handicap’ signal their ‘good genes’ through the display of the costly masculine or feminine features in men or women respectively.

Men show strong and consistent preferences for more feminine women's faces (Grammer and Thornhill 1994; Fink and Penton-Voak 2002; Rhodes 2006), where women’s femininity is also assumed to signal fertility (Rhodes 2006; Law Smith et al. 2006). This preference is reported to be consistent across different cultures, but it is stronger in urban regions and industrialized countries with higher health index (Marcinkowska et al. 2014; Scott et al. 2014; Dixson et al. 2017). However, a recent study reports contrary results (Jones and Jaeger 2019), where the participants showed no preference for more feminine faces either in a two-alternative forced-choice setup or using a rating method. In women, however, there is considerable variation among individuals in their preference for male faces with masculine features (Thornhill and Gangestad 1993; Penton-Voak et al. 2001; Fink and Penton-Voak 2002; Koehler et al. 2004; DeBruine et al. 2010a; Gildersleeve et al. 2014). While higher male masculinity may signal health and advertise good genes (based on the immunocompetence handicap hypothesis discussed above), lower masculinity may indicate higher male investment in long term relationships, higher parental quality and honesty (Booth and Dabbs 1993; Perrett et al. 1998; Gray et al. 2002; Smith et al. 2009; Pollet et al. 2011). Therefore, this trade-off between the costs and benefits of opting for a masculine partner (i.e., the choice for direct vs. indirect benefits) has led to the hypothesis that women’s preferences should be condition-dependent, in the sense that preferences for more masculine males are expected in the context of reproduction and/or when the presumed costs of masculinity outweigh the indirect genetic benefits (Little et al. 2007). More specifically, women are expected to prefer more masculine men during their fertile period (Penton-Voak and Perrett 2000), and in the context of short-term relationships (Little et al. 2002).

The use of hormonal contraception is also thought to affect preferences for masculinity. Studies report that hormonal contraceptives cause a hormonal state similar to pregnancy by increasing the progesterone levels, which leads to preferring the direct benefits of long term and caring partners, and thus a preference for men with less masculine features (Little et al. 2011, 2013), while others have not found any preference (Cobey et al. 2015; Jones et al. 2018). There is other evidence that also questions the validity of this hypothesis; A study shows that mothers with young infants prefer bearded men when judging fathering abilities (Dixson et al. 2019), and it has been shown that beardedness exerts stronger effects on judgments of masculinity, dominance and aggressiveness compared to facial masculinity (Mefodeva et al. 2020). Additionally, most of the studies that suggest an effect of women’s hormonal state on their preference for masculinity have used indirect survey methods to characterize women’s fertility (Gildersleeve et al. 2014) while surveys do not accurately capture women’s fertility (Blake et al. 2016). Recent studies that have employed hormonal measures to assess women’s fertility have not found any significant correlation between women’s hormonal state and their preference for masculinity (Marcinkowska et al. 2018; Dixson et al. 2018b; Dixson et al. 2018a).

Relationship status has also been reported to have an effect on masculinity preference, as women in a steady relationship would assess other men as a possibility to engage in extra-pair copulations (i.e., seeking genetic benefits) and thus should prefer more masculine phenotypes (Little and Hancock 2002; Sacco et al. 2012). Additionally, the type of relationship (short- vs. long-term) could also affect these preferences (Holzleitner and Perrett 2017).

There have also been reports of a positive correlation between preference for masculine faces and self-perceived attractiveness (Little et al. 2001), as well as unrestricted sociosexuality (Provost et al. 2006; Marcinkowska et al. 2019). However, a recent study has reported no association between self-perceived mate value and women’s preference for masculinity (Clarkson et al. 2020), while another study reports a positive correlation between women’s masculinity preference and their self-perceived attractiveness but not their third-party rated attractiveness (Docherty et al. 2020). Similarly, a recent study reports no association between sociosexuality and preference for masculinity in women (Stower et al. 2020).

Early puberty, often known as a sign of psychological or physical stress, has also been reported to cause a preference for short-term relationships, and consequently higher masculinity (Batres and Perrett 2016; Niu and Zheng 2020), although other studies have not found any correlation (Cornwell et al. 2006).

These effects, however, are not the only (or maybe even the main) causes of variation in masculinity preference. A recent twin study showed that a high percentage of the between-women variation in preferences for masculine traits was explained by genetic variation (38%), in contrast to very weak condition-dependent effects (<1%) (Zietsch et al. 2015). Additionally, some recent studies have questioned the applicability of ICHH in humans and have reported contradicting results (Rantala et al. 2013; Nowak et al. 2018; Zaidi et al. 2019), thus challenging the hypothesis that extreme sex-typical features should appear more attractive in a genetic benefits context. It is therefore not unexpected to find weak or no effects of the condition-dependent variables on the masculinity preference variation.

Furthermore, some studies have reported higher attractiveness in faces that show average features (Baudouin and Tiberghien 2004). In a recent study, Holzleitner & Perrett (2017) investigated attractiveness as a function of different levels of masculinity and showed that there is an overall tendency towards masculine faces. However, when facial features became more extreme (i.e. more feminine or more masculine) the attractiveness dropped. It is thus crucial to take the initial masculinity of the faces into consideration, an aspect that has been studied only sporadically in this context.

In this study, we examine women’s preferences for masculinity using manipulated 3D scans of male faces. We investigate possible effects of a number of condition-dependent factors, including self-perceived attractiveness, use of hormonal contraception, relationship status, preferred type of relationship for single women, age of menarche and the willingness to engage in uncommitted sexual relationships. In a similar study, Van Dongen et al. (2019) reported low between-women variation (around 2%) when using 3D scans, while the results of Zietsch et al. (2015) indicate much higher variation. Therefore it is necessary to check for the repeatability of the results and make sure that the 3D scans used for the study convey enough information on sex-typical features in order to evoke effects in masculinity preference. We used scans with different initial levels of masculinity as stimuli. If slightly above average masculinity is generally preferred (Holzleitner and Perrett 2017) we also expect to see a decreased preference for extreme levels of masculinity or femininity. More specifically, we expect that when the initial masculinity of the face is high, a preference for a more feminized version of the face would emerge, while for faces with low initial masculinity, women would prefer the masculinized version. For scans with intermediate initial masculinity, no preference is predicted. Finally, we also test for interactions between the initial masculinity and the condition-dependent covariates, an aspect that has not been studied before. It is worth noting that the tested factors are the ones that are mostly investigated in the literature, however, the list is not exhaustive.

2. Materials and Methods

2.1. Participants

Students from the University of Antwerp (under-graduate, graduate and post-graduate) were invited by e-mail to participate in our online questionnaire in November 2018. Overall, 945 women participated in our study. They were asked to anonymously fill in a questionnaire providing their age, age of menarche, weight, height, sexual orientation, relationship status, whether they use hormonal contraception, and to rate their own attractiveness on a scale of 1 (not attractive) to 10 (very attractive). Single women also indicated which type of relationship they prefer in the near future (long-term, short-term or no relationship). They were also asked to complete the 9-item revised Sociosexual-Orientation Inventory (SOI-R) which provides a measure of their sociosexual behavior, attitude and desire (Penke and Asendorpf 2008). All individuals were informed about the study procedure and signed an informed consent document. Data collection and handling were approved by the ethics committee of the University of Antwerp. A sample questionnaire is included in supplementary materials.

2.2. Stimuli and rating system

We used a dataset of 3D face scans consisting of 1246 adults (623 males and 623 females) between the ages of 18 and 35 and a BMI within the normal range (18.5 – 24.9 kg/m2), all with self-reported European-derived ancestry and no history of any significant facial trauma or facial surgery, or any medical condition that might alter the facial structure. These scans originated from a mixture of several studies at the University of Pittsburgh, Pennsylvania State University and Indiana University-Purdue University, Indianapolis and were captured using two stereo-photogrammetry systems: the VECTRA H1 camera (Canfield Scientific, Parsippany, NJ, United States) and the 3dMDface system (3dMD, Atlanta, GA, United States). The following local ethics approvals were obtained for gathering the facial scan data: University of Pittsburgh (IRB PRO09060553 and RB0405013); UT Health Committee for the Protection of Human Subjects (HSC-DB-09-0508); Seattle Children’s (IRB 12107); University of Iowa Human Subjects Office (IRB 200912764 and 200710721); Urbana-Champaign, IL (IRB 13103); New York, NY (IRB 45727); Twinsburg, OH (IRB 2503); State College, PA (IRB 44929 and 4320); Austin, TX (IRB 44929); and San Antonio, TX (IRB 1278); Indiana University Purdue University Indianapolis (IRB 1409306349).

A spatially dense facial template comprised of n=7160 paired vertices was non-rigidly mapped onto each of the faces in the dataset (Ekrami et al. 2018). This step allows us to represent the facial scans using a homogeneous configuration of quasi-landmarks. Next, we applied a Partial Least Squares (PLS) regression to estimate the masculinity-femininity vector of the standardized faces and calculated a masculinity score for each face. The masculinity scores and manipulations were done in MATLAB R2019b. Figure 1 shows the distribution of the masculinity scores for male and female faces, as well as a heat-map portraying the regions of difference between the average male and female faces. From the male faces, we randomly chose a total of 10 scans with 5 different levels of initial masculinity ranging from very low (mean – 2*SD), low (mean – SD), average (mean), high (mean + SD) to very high (mean + 2*SD) (Fig1). The masculinity level of each of these 10 scans was then increased and decreased along the masculinity vector by a standard deviation unit. This resulted in 10 pairs of faces, each pair representing a masculinized and feminized version of the same face.

Fig1.

Fig1

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Variation in facial masculinity. (a) Distribution plot of the masculinity scores for male and female faces. The 5 levels of initial masculinity are indicated, and a sample face is used to demonstrate average femininity and average masculinity. (b) Heat-map showing the regions of difference between the average male and average female face.

The two versions of each face were represented to the participants next to each other, displaying the face profile in a front and a three-quarters view. These faces were displayed as gray 3D objects to avoid any effects of the skin or eye color on perceived attractiveness (Fig2). We used a standard forced-choice technique commonly cited in the literature to measure preference for masculinity (Zietsch et al. 2015). The participants were asked to indicate which version of the face do they find more attractive using an 8-point scale (1= left is much more attractive, 8 = right is much more attractive; 4 or 5 = slight preference for either side). The left/right order of the faces as well as the initial masculinity order of the stimulus pairs were randomized to avoid any bias in the results.

Fig2.

Fig2

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An example of the faces presented to the participants for attractiveness ratings (this face was chosen from the low initial masculinity group). The faces on the left present the feminized version, and the faces on the right show a masculinized version.

The statistical power of this study design largely depends on the number of stimuli the participants evaluate, which is relatively low. In order to have an internal check and a confirmation that we do have sufficient power to detect moderate effects, the use of hormonal contraception is an important covariate. Because many studies have confirmed the effect of hormonal contraception, this covariate is very likely to contribute to explaining variation in the preferences for masculinity. If the use of hormonal contraception does contribute significantly and in the expected direction in our analyses, this suggests that the power of our analyses is sufficient to detect moderate effects.

2.3. Statistical analysis

For our statistical analysis, we only used the results from participants between the ages 18 to 35 to match the age range of the used facial scans. We also limited our study to heterosexual women. We used linear mixed models to examine masculinity preference and the possible links to condition-dependent variables. The attractiveness ratings were transformed to the range of −4.5 to +4.5, such that negative scores reflect a preference for feminized faces and positive scores a preference for masculinized faces. An average score of zero reflects no preference at all. These scores were used in the models as the dependent variable.

The first model i.e. the null model, was defined to calculate the repeatability of the preference scores. In this null model, only the participant ID and the scan ID were used as random effects. The repeatability was calculated as the between-individual variance divided by the sum of the between-individual variance and residual variance. The intercept of this model estimates the overall degree of preference for masculinity, taking between-individual and between-scan variation into account.

In a second model, the initial masculinity of the faces was added as a fixed effect (categorical - 5 levels) with scan ID now added as random effect nested in initial masculinity. This model allows us to explore the hypotheses that preferences would be oriented towards average faces. We calculated the proportion of variation in preference scores among scans explained by the initial masculinity as the decrease in between-scan variance divided by the between-scan variation in the null model.

In our third model (full model), condition-dependent effects (perceived self-attractiveness, use of hormonal contraceptives, relationship status, age of menarche and SOI scores) were added as fixed effects. The use of hormonal contraceptives and relationship status were both effect-coded in this model1. The proportion of between-women variation in preferences for masculinity explained by these covariates was calculated as the decrease in between-women variance divided by the between-women variance in the null model. Finally, we tested for interactions between the condition-dependent variables and the initial level of masculinity of the faces.

3. Results

In total, 945 women filled in the questionnaire, from which 793 reported to be heterosexual and between the ages of 18 and 35 and thus used in our study. The average age of the participants was 22.3 years (SD = 3.3 years). A total of 442 (55.7%) reported to be in a steady relationship. Of the participants not in a steady relationship (351), 228 (64.9%) were looking for a steady relationship, 32 (9.1%) were interested in short-term relationships and 91 (25.9%) were not interested in any kind of relationship at the moment of the study. A total of 593 (74.7%) reported to be using hormonal contraceptives, out of which 376 (63.4%) were in a steady relationship. A detailed description of the results is provided as supplementary materials.

The results of the statistical models are reported in Table1. The intercept of the null model indicated a slight but statistically insignificant preference towards the masculine faces (Cohen’s d = 1.05, p=0.06). The repeatability of between-women variation in preferences for masculinity equaled 17.2%. Initial masculinity of the faces affected preferences for masculinity (F5,5 = 6.98, p=0.02). The participants showed a significant preference for the masculinized face in the very low masculinity group and the average group (Fig3). There was no significant preference in the other groups (Table 1, Fig3). Pairwise comparisons (based on least-squares means) showed that the very low and average group (letter a in Fig3) differed significantly from the high and very high group (letter b in Fig3) but the low group did not differ significantly from the other groups (letters ab in Fig3). The effect of the initial masculinity explained 67.5% of the between-scan variation.

Table 1-.

Test of associations between preference for masculinity and condition-dependent variables in women. The intercept in the null model shows the average ratings without considering any effects. In the initial masculinity model, the initial masculinity of each of the stimuli is added as a fixed effect. Finally, in the full model, the condition-dependent explanatory variables were added. Predicted effects are added when applicable. The estimate of the fixed effects and their standard error (in parenthesis) are reported. Significant effects are reported in bold.

Effect First model
(Null model)		 Second model
(Initial masculinity effect)		 Prediction Third model
(Full model)
Effect Size CI (95%) p-value Effect Size CI (95%) p-value Effect Size CI (95%) p-value
Intercept 0.43 (0.20) [0.01, 0.84] 0.061
Initial masculinity very low 1.00 (0.26) [0.60, 1.40] 0.012 ++ 0.89 (0.48) [0.01, 1.77] 0.071
Low 0.32 (0.26) [−0.08, 0.72] 0.281 + 0.20 (0.48) [−0.67, 1.08] 0.676
Average 1.11 (0.26) [0.70, 1.51] 0.008 0 0.99 (0.48) [0.11, 1.87] 0.045
High −0.25 (0.26) [−0.65, 0.15] 0.389 − −0.36 (0.48) [−1.24, 0.51] 0.461
Very high −0.03 (0.26) [−0.43, 0.37] 0.920 −− −0.14 (0.48) [−1.02, 0.73] 0.772
Self-perceived attractiveness + −0.00 (0.02) [−0.04, 0.04] 0.982
Hormonal contraception (yes) −(yes) −0.16 (0.06) [0.03, 0.30] 0.013
Socio-sexuality Behavior + 0.01 (0.01) [−0.01, 0.03] 0.343
Attitude + 0.01 (0.01) [−0.00, 0.03] 0.172
Desire + 0.00 (0.01) [−0.02, 0.02] 0.924
Average + 0.03 (0.01) [0.00, 0.05] 0.017
Relationship status (steady/single) + (steady) −0.00 (0.06) [−0.12, 0.12] 0.943
Age of menarche − −0.02 (0.01) [−0.06, 0.01] 0.198
σID2 0.4 0.4 0.39
σScan2 0.4 0.13 0.13
σresidual2 1.92 1.92 1.92
Repeatability: 17.2% 17.2% 16.8%
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Fig3.

Fig3

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Comparison of the masculinity preference scores based on the initial masculinity of the faces. For each level of masculinity, the mean and 95% confidence intervals for preference for masculinity are plotted. Letters a and b indicate groups that differ significantly from each other.

With respect to the condition-dependent effects, the use of hormonal contraception resulted in a decreased preference for masculinity, meaning women who did not use hormonal contraceptives showed a relatively strong preference towards the more masculine faces (d = −0.32, F1,787 = 6.00, p = 0.011). In addition, participants’ average SOI scores correlated positively with preference for masculinity, where women with a less restricted sociosexuality prefer more masculine faces (d= 0.06, F1,787 = 5.65, p = 0.017). However, each of the three dimensions of sociosexuality, namely behavior, attitude and desire, individually did not show any significant effect on masculinity preference. None of the other explanatory variables (self-perceived attractiveness, relationship status and age of menarche) were found to have any significant effects on masculinity preference (Table 1). Of the women not in a steady relationship, their preferred type of relationship in the near future also did not affect their preference for masculinity (F2,343 = 1.08, p=0.33). Based on the reduction in between-women variation when adding the condition-dependent effects to the model, the latter explained 2.5% of the variation in preferences for masculinity. Additionally, we found no interaction between the initial masculinity level and any of the condition-dependent variables (details not shown).

4. Discussion

The condition dependency of women’s preference for masculinity has been studied repeatedly by examining various covariates. On one hand, masculine features in a man have been argued to be a sign of genetic quality, as developing these features would introduce a high biological cost that individuals of low genetic quality cannot bear. On the other hand, men with less masculine features are thought to show higher parental quality. This is due to lower testosterone levels in these individuals, which is associated with better parental skills (Muller et al. 2009). These contrasting costs and benefits of preferring male masculine traits are believed to have caused the evolution of between-individual variation in women’s preference for masculinity and observed condition-dependent effects (Little et al. 2011; Zietsch et al. 2015).

In this study, we examined the effects of condition-dependent variables and the initial masculinity of male faces on preference for masculinity in women. On average, without considering any covariates, there was no significant overall preference for masculine or feminine faces. This finding is in line with some previous studies (Little et al. 2001; Scott et al. 2010) and in contrast to some others that have reported a tendency towards more masculine faces (Rhodes et al. 2003; DeBruine et al. 2006; Holzleitner and Perrett 2017) or feminine faces (Rhodes et al. 2000; Little and Hancock 2002; Penton-Voak et al. 2003). This difference might be due to different stimuli used in these studies (Johnston et al. 2001; Rhodes 2006), where they mostly use 2D photographs with a limited number of landmarks as opposed to our spatially dense 3D approach. Also, the initial masculinity of the faces might have played a role, which is supported by our results.

When the initial masculinity of the faces was analyzed, an interesting pattern emerged. Overall, preferences for masculinity in the different initial masculinity levels tend to fall into two separate groups (Fig3; although the results for the low masculinity group is inconclusive). This can mean that attractiveness and facial masculinity tend to have a threshold-like correlation, rather than a linear or curvilinear one. In other words, women show a preference for faces with higher masculinity, if the faces have low masculinity levels. As the masculinity level of the face increases, this preference for more masculinity fades away and results in no preference. Previous studies using 2D photos of faces have also suggested a threshold model in masculinity preference (Dixson and Brooks 2013; Dixson et al. 2016). These findings could be an indicator of the trade-off between better genes (expressed by higher masculinity) and better parental investment (associated with lower masculinity). Based on our results, extreme levels of masculinity are not perceived as attractive (therefore not a sign of fitness) and faces with average or slightly higher masculinity were generally preferred. From an evolutionary point of view, averageness can be seen as a sign of heterozygosity, i.e. genetic diversity (Thornhill and Gangestad 1993) and may act as a health certificate because of a stabilizing effect of natural selection on facial features (Symons 1980; Langlois and Roggman 1990; Baudouin and Tiberghien 2004). Furthermore, a recently published article also reports finding no evidence of sex hormones acting as immunosuppressant in men (Nowak et al. 2018), therefore challenging the ICHH and the possible correlation between masculinity and attractiveness (Scott et al. 2013).

Our findings also suggest that women not using hormonal contraceptives preferred more masculine faces. This is in line with our expectation, since using hormonal contraceptives results in a hormonal state similar to pregnancy and pregnant women are expected to choose partners with better parental investment. There are several studies linking low masculinity to better parental investment. Reports show a link between testosterone level in males and marital problems (Mazur and Michalek 1998) and masculinity as a signal of lower suitability as a long-term partner or parent (Perrett et al. 1998; Johnston et al. 2001). A recent study has even found a preference for less masculine faces in post-partum women compared to their pregnancy period (Cobey et al. 2015), which further confirms the hypothesis that men with less masculine faces are perceived as better parents. However, it should be reiterated here that we have used a survey-based, indirect method to gather information about women’s hormonal state, which has been reported to be inaccurate (Blake et al. 2016). Considering that the recent studies using hormonal measures to assess women’s fertility report contrary results to ours (Marcinkowska et al. 2018; Dixson et al. 2018b; a), we must interpret our results with caution.

Additionally, while none of the sub-categories of sociosexual orientation showed any effect, the cumulative effect of these categories, i.e. the average SOI score showed a weak but statistically significant correlation with the masculinity preference in the expected direction. This means that women with a less restricted sociosexual orientation showed a slight preference for more masculine faces. This result is in line with the majority of studies on this subject (Provost et al. 2006; Marcinkowska et al. 2019), however other studies have reported no correlation between SOI and masculinity preference (Glassenberg et al. 2010; Stower et al. 2020).

On the other hand, we found no significant effect of all other condition-dependent effects, including self-perceived attractiveness, relationship status and age of menarche on preference for masculinity. This is in line with our previous finding, since the possible correlation between these covariates and preference for masculinity are all based on the hypothesis that masculinity is perceived as more attractive, which our results do not fully support. Additionally, none of the covariates showed any interaction with the initial masculinity. This finding confirms that different levels of initial masculinity do not affect the association between condition-dependent variables and preference for masculinity. Overall, the low percentage of explained variance (2.5%) here is in line with the findings of Zietsch et al. (2015), where they suggest that most of the variation in masculinity preference is explained by genetics rather than condition-dependent effects.

It is important to note that in our study, each initial masculinity group consisted of only 2 faces, therefore caution must be taken in interpreting the results. Part of the found effects in groups, or even lack of effect in the “low” group, may be attributed to idiosyncratic features unique to the 2 individuals in that initial masculinity group. Therefore, we would expect to see stronger effects if similar studies were conducted with a higher number of faces in the initial masculinity groups. The fact that we did find an effect of hormonal contraception in the expected direction confirms that our sample size is large enough to detect the most common effect in women’s masculinity preference. The lack of effects in the other covariates at least suggests that their effects are likely to be less important than that of hormonal contraception. Additionally, it should be noted that the participants of this study formed a highly homogenous sample group (mostly Belgian students, mostly of European descent). Studies have shown that the condition-dependent variables may have bigger effects in different cultures (Penton-Voak et al. 2004), or countries with lower health index (DeBruine et al. 2010b). Future studies on this subject can benefit from more diverse sample groups.

In conclusion, our data show that women find men with average and slightly higher levels of masculinity more attractive, while extreme levels of masculinity are not preferred. However, the long-standing immunocompetence handicap hypothesis that claims more masculine men should appear more attractive to women, when they consider themselves as attractive, are single and looking for short term relationships and/or had an early age of menarche was not supported by our findings. Only the effect of hormonal contraception and average SOI score were statistically significant in the expected direction, and all condition-dependent effects explained only a small amount of the between-women variation in preferences for masculinity. Thus, the relative importance of the different conditions presumed to affect masculinity preferences may be mainly dominated by the use of hormonal contraception.

Supplementary Material

Questionnaire
Maculinity Scores
Questionnair Results
Questionnair Data

Footnotes

1

No significant difference emerged in the results when we used dummy-coding for these two variables

6. Data availability

The developed algorithms used to perform this analysis are available in the following GitHub repository: https://github.com/omidek/FA_calculation/. We are unable to provide the raw facial scans’ data that are part of the Penn State University dataset because these data are highly identifiable in nature and are in legal and ethical violation of the informed consent obtained from the participants. Interested and qualified researchers may send requests for a more confined sharing of the data to the authors. 3D surface images gathered at the University of Pittsburgh for the 3D Facial Norms cohort are available through the FaceBase Consortium (www.facebase.org) under accession number FB00000491.01, or at https://www.facebase.org/data/record/#1/isa:dataset/accession=FB00000491.01.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Questionnaire
NIHMS1644675-supplement-Questionnaire.docx (44.9KB, docx)
Maculinity Scores
NIHMS1644675-supplement-Maculinity_Scores.tif (999.8KB, tif)
Questionnair Results
NIHMS1644675-supplement-Questionnair_Results.tif (912.3KB, tif)
Questionnair Data
NIHMS1644675-supplement-Questionnair_Data.xlsx (126.5KB, xlsx)

Data Availability Statement

The developed algorithms used to perform this analysis are available in the following GitHub repository: https://github.com/omidek/FA_calculation/. We are unable to provide the raw facial scans’ data that are part of the Penn State University dataset because these data are highly identifiable in nature and are in legal and ethical violation of the informed consent obtained from the participants. Interested and qualified researchers may send requests for a more confined sharing of the data to the authors. 3D surface images gathered at the University of Pittsburgh for the 3D Facial Norms cohort are available through the FaceBase Consortium (www.facebase.org) under accession number FB00000491.01, or at https://www.facebase.org/data/record/#1/isa:dataset/accession=FB00000491.01.

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