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Article

The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences

by
Luis Carlos Pereira Monteiro
1,2,
Victória Elmira Ferreira do Nascimento
3,
Amanda Carvalho da Silva
4,
Ana Catarina Miranda
5,
Givago Silva Souza
2,4 and
Rachel Coelho Ripardo
5,*
1
Neuroscience and Cell Biology Graduate Program, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil
2
Center for Tropical Medicine, Federal University of Pará, Belém 66055-240, Brazil
3
Faculty of Psychology, Institute of Philosophy and Human Sciences, Federal University of Pará, Belém 66075-110, Brazil
4
Faculty of Biological Sciences, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil
5
Neuroscience and Behavior Graduate Program, Center for Behavioral Theory and Research, Federal University of Pará, Belém 66075-110, Brazil
*
Author to whom correspondence should be addressed.
Symmetry 2022, 14(2), 423; https://doi.org/10.3390/sym14020423
Submission received: 6 December 2021 / Revised: 27 December 2021 / Accepted: 5 January 2022 / Published: 20 February 2022
(This article belongs to the Section Life Sciences)
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Abstract

:
Humans, like other species, have a preference for symmetrical visual stimuli, a preference that is influenced by factors such as age, sex, and artistic training. In particular, artistic training seems to decrease the rejection of asymmetry in abstract stimuli. However, it is not known whether the same trend would be observed in relation to concrete stimuli such as human faces. In this article, we investigated the role of expertise in visual arts, music, and dance, in the perceived beauty and attractiveness of human faces with different asymmetries. With this objective, the beauty and attractiveness of 100 photographs of faces with different degrees of asymmetry were evaluated by 116 participants with different levels of art expertise. Expertise in visual arts and dance was associated with the extent to which facial asymmetry influenced the beauty ratings assigned to the faces. The greater the art expertise in visual arts and dance, the more indifferent to facial asymmetry the participant was to evaluate beauty. The same effect was not found for music and neither for attractiveness ratings. These findings are important to help understand how face aesthetic evaluation is modified by artistic training and the difference between beauty and attractiveness evaluations.

1. Introduction

Symmetry permeates various elements that can be perceived by humans and can be present in natural or artificial objects [1]. Many researchers have investigated how humans perceive and react to symmetry, especially the symmetry of visual stimuli [2]. This type of symmetry is defined by patterns of repetition through one or more axes in an image [3]. Symmetry based on only one axis, also called bilateral symmetry, is a topic of special interest in the study of visual symmetry, as it is usually more quickly processed and better evaluated than symmetry measures based on more axes [4].
However, symmetry does not have to be just visual or spatial. Examples of symmetry can be found in musical compositions and movements, such as in dance. In music, symmetry is an almost basic condition for its production, as symmetrical patterns are constantly observed in several classical works over the centuries, anchoring melodic processes, rhythm, and harmonic constructions [5,6]. For dance, laterality is an important condition to dictate the direction of choreographic productions. Symmetry, in this case, would be linked to the coordination and use of both sides of the body, an orientation that would even reflect the functional organization of the nervous system [7,8].
The preference for symmetry is evident among several taxa, such as insects [9,10,11], fishes [12,13,14], birds [15,16], non-human primates [17,18], and humans [1,2]. In particular, the human preference for symmetry has been widely studied [2]. It is supported by cross-cultural studies but varies according to the stimulus type [19]. Symmetrical patterns tend to be preferred in abstract patterns [20,21,22], flowers [23], faces [24], and, to a lesser extent, in works of art and landscapes [25,26]. The preference for symmetry is evident not only through explicit evaluations, such as numerical classifications but also through implicit evaluations, such as the relationship between symmetry and positive valence words [27].
The literature presents two non-mutually exclusive explanations for the preference for symmetry in humans: the theory of evolutionary advantage and the theory of perceptual bias (see Treder [1] for a literature review). The theory of evolutionary advantage relates the preference for symmetry to sexual selection, with symmetry being a sign of genetic quality and stability of the physical development of possible partners [28]. This hypothesis is supported by face evaluation studies, which find a consistent positive relationship between attractiveness and symmetry in different cultures [29,30,31]. However, that relationship seems to change through the different phases of human development [32,33,34]. The perceptual bias theory, in turn, relates the preference for symmetry as a by-product of the ease of processing symmetrical objects by the visual system [35]. Such objects would require less processing effort, as they present repeated visual information. This processing facility is often called perceptual fluency [36,37].
However, there are conflicting results in other studies. For example, the results of several studies have failed to find the relationship between facial and body symmetry with health, going against what was expected for the evolutionary advantage view [38,39]. On the other hand, some research has found gender differences in the assessment of symmetry in neutral objects [40], abstract patterns [41], and faces [42,43]. Such mixed results indicate that sexual selection and perceptual fluency, alone, are not sufficient to explain the preference for symmetry.
In addition, small degrees of asymmetry are always found in natural organisms considered symmetrical [44]. Therefore, studies manipulating faces and landscapes [25,45] for perfect symmetry show that this is not preferred, as perfect symmetry can be perceived as artificial or unnatural [46]. This preference for symmetry, moreover, can be affected by different factors, such as age [47,48], sex [41], and the observer’s expertise in arts [49,50,51].
Art expertise, which involves theoretical knowledge and amateur or professional experiences in arts, is one of the inter-individual differences that influence aesthetic experience [52]. Several models trying to understand the factors affecting aesthetic appreciation and judgment suggest the importance of art expertise [53,54,55,56,57]. In fact, experienced artists differ from the untrained population in several aspects: in the judgment [58,59], interest [60,61], and emotional response [62] to works of art, as well as in visualization strategies [63,64], in the preference for abstract or representational images [65,66], and the preference for complexity [67,68]. In addition, there is evidence that art expertise modulates neural activity during aesthetic appreciation and artistic creation for the visual arts [69,70], music [71,72], and dance [73,74].
It has been observed that the greater the artistic expertise in visual arts, the more beauty is attributed to objects that are not symmetrical [49,50,51]. The increase in complexity and abstraction, and, at some level, the symmetry variation in works of art seem to impact lay people more negatively than experienced visual and other artists [75]. The influence of art expertise in the evaluation and aesthetic preference is also observed in other artistic modalities, such as music, in the evaluation of sounds [76,77], and dance, in the evaluation of movement [78,79].
Recent research has found that the preference for symmetry in abstract images is less pronounced in visual artists than in the general population [49,50,51], but it could be asked if this pattern would also be applied for the preference for symmetry in human faces in visual artists. In addition, it is well established that cross-modal interactions between different sensory modalities can impact individual perception [80,81] and that stimuli modality-specific (visual/auditory) effects on aesthetic judgments have already been reported [82]. Then, it is reasonable to question how the expertise in non-visually dominant arts such as music and dance would have an impact on the symmetry visual perception and visual aesthetic judgments. So far, it is unknown how the level and type of art expertise could influence the preference for facial symmetry. Thus, the present study aims to assess how the perception of beauty and attractiveness for human faces is influenced by the symmetry of the observed face and the observer’s expertise in different types of arts.

2. Materials and Methods

2.1. Participants

A total of 116 participants recruited online through a university mailing list (registration form in Supplementary file S1) were tested in person. Participants were between 18 and 51 years old (mean ± SD = 24.3 ± 5.27), similarly distributed to biological sex (55 male, 60 female, 1 preferred not to respond), predominantly residents in the capital of the state of Pará, Brazil (n = 95; 81.9%), mostly university students (n = 68; 58.6%), most (n = 82; 70.7%) had an individual monthly income of less than one Brazilian Minimum Wage (R$ 998.00 in 2019) and a family monthly income between 1 and 2 minimum wages (n = 37; 31.9%), had normal or corrected to normal vision, did not use psychotropic drugs, did not have a diagnosis of neuropsychiatric diseases, and most were predominantly heterosexual (n = 66; 56.9%), with the remainder being similarly distributed among bisexuals (n = 26, 22.4%) and predominantly homosexuals (n = 24, 20.7%).

2.2. Instruments

2.2.1. Sociodemographic Questionnaire

The sociodemographic questionnaire is in the supplementary materials (Supplementary file S2) and aims to obtain general information about the participants regarding age, sex, marital status, and education, among others. To confirm that the participant was able to carry out the research, the questionnaire also included questions about visual problems and previous diagnoses of neuropsychiatric diseases, as well as about the use of psychotropic drugs.

2.2.2. Arts Expertise Questionnaire

The Arts Expertise Questionnaire is in the supplementary materials (Supplementary file S3) and aims to stipulate the participant’s expertise in three artistic modalities: visual arts, music, and dance. It presents items related to formal education, professional experience, and skills in the artistic modalities of interest. This instrument was based on the Art Expertise Questionnaire [70] that include questions for visual arts and music. In the present work, we added adapted questions to also assess dance expertise. The instrument has 45 objective items divided equally into three sections: visual arts, dance, and music (i.e., 15 items for each section). The sum of responses related to visual arts, dance, or music was used as the score of expertise in each art modality.
To ensure the validity of the questionnaire in measuring expertise, we also apply the following validated instruments: (i) Part A (art interest) of the Vienna Art Interest and Art Knowledge Questionnaire (VAIAK) [83] and (ii) Visual arts, Music and Dance sections of the Creative Achievement Questionnaire (CAQ) [84]. The scales mentioned were used since art interest and creativity are variables related to art expertise [59,83]. Each participant was also classified as a layperson (n = 48), visual artist (n = 27), dancer (n = 19), or musician (n = 22) according to their professional area or academic background in the artistic modalities (items 13 and 14 of the sociodemographic questionnaire, respectively). Thus, to be classified as an artist in one of the art modalities (visual arts, music, or dance), the participant needed to be an undergraduate student or a professional in that art modality.

2.3. Stimuli

For the present study, we selected 100 photos of faces (50 female and 50 male) with neutral expression from the Chicago Face Database (CFD). The CFD includes 597 high-resolution photographs of faces of men and women of different ethnicities, aged between 18 and 65 years. The CFD was initially built by Ma et al. [85] and is continuously updated at chicagofaces.org. The facial asymmetry measurement of each face was obtained using geometric morphometric techniques for object symmetry as described in Klingenberg [86]. Object symmetry implies that both sides of the face are mirror images of each other. To run this analysis, the Face++ API (Megvii Technology, faceplusplus.com, accessed on 20 December 2021) was used to automatically add 83 landmarks on selected faces (Figure 1A). Face++ API has been shown to be reliable for obtaining facial landmarks in other works [87,88].
Following this, data from facial landmarks coordinates underwent a Procrustes superimposition—a technique that adjusts the scale, rotation, and translation of shapes without losing their biological variability. These new coordinates data resulting from the Procrustes superimposition were used to calculate facial asymmetry using a mixed two-way ANOVA model. The factors of this ANOVA were the individual and the side of the face from which the landmarks were extracted. As this ANOVA was performed using the Procrustes superimposition data, it is called Procrustes ANOVA. From Procrustes ANOVA it is possible to extract individual facial asymmetry scores, being one measure of absolute asymmetry based on the Procrustes distances and one measure of the relative magnitude of asymmetries based on Mahalanobis distance that is corrected for error variance in the sample. Procrustes superimposition and Procrustes ANOVA were performed in the MorphoJ software [89]. For this study, we used Mahalanobis distances, which avoid multicollinearity problems [90]. The full distribution of the facial asymmetry scores is shown in Figure 1B with examples of faces with the minimum, median, and maximum asymmetry.

2.4. Procedure

2.4.1. Instruments Application

At first, the participant responded on a computer in a room at the Federal University of Pará through the Google Forms web application to the (i) Informed Consent Form; (ii) Sociodemographic Questionnaire, and (iii) Arts Expertise Questionnaire with CAQ and VAIAK subscales (Figure 1C). Data collection was carried out in an air-conditioned room at the Behavior Theory and Research Center of the Federal University of Pará. The room had a table, a CPU, and a screen monitor, where the questionnaires were applied and where the face evaluation was carried out. The screen resolution was 1024 × 768 pixels with a 60 Hz refresh rate. Participants were positioned at a fixed distance of 55 cm from the screen during image evaluation. Participants completed the instruments using a standard wired keyboard and computer mouse. The participants respond to the instruments in private, without interference from the researcher responsible, who left the room during the procedure.

2.4.2. Face Evaluation Task

After the application of the Informed Consent Form, Sociodemographic Questionnaire, and Arts Expertise Questionnaire, the face evaluation task was explained to the participant. Subsequently, the participant was asked to briefly explain the task to be performed, in order to ensure understanding. Most participants understood the experiment and, in cases where there were doubts, these were resolved before the experiment was carried out.
The presentation of stimuli and behavioral data collection was prepared using MATLAB software version R2018a [91]. In the face evaluation task, 100 photos of human faces (50 female and 50 male) with different levels of symmetry were sequentially presented to each participant in a random order (in order to prevent some photos from being recurrently better evaluated than others by presentation order bias). For each of these photos, the participant evaluated the beauty and attractiveness of the face presented. This task followed the following sequence, repeated for each face (Figure 1C): (i) presentation of a fixation cross for 1 s, (ii) presentation of the face for 3 s, (iii) presentation of the question ‘How beautiful do you think is the face you just saw?’ along with a rating scale from 1 to 7 (from not beautiful to extremely beautiful) until the participant’s answer; and (iv) presentation of the question ‘How attractive do you think is the face you just saw?’ along with a rating scale from 1 to 7 (from not attractive to extremely attractive) until the participant’s answer. Once the question was answered, it was no longer possible to modify the answer, as the presentation immediately moved to the subsequent screen.

2.5. Data Analysis

We used One-way ANOVA with Tukey HSD post hoc tests to compare the scores of laypersons, visual artists, dancers, and musicians on the Arts Expertise Questionnaire in order to verify the validity of this instrument for measuring expertise (see Section 2.2.2 Arts Expertise Questionnaire for an explanation of how these groups were divided). We used Kruskal-Wallis with Dunn’s post hoc tests to compare the scores of laypersons, visual artists, dancers, and musicians on Part A of VAIAK and visual arts, music, and dance sections of the CAQ. A non-parametric strategy was used since assumptions of normality and homoscedasticity were not satisfied. Furthermore, associations between art expertise, creativity (CAQ), and art interest (Part A of VAIAK) scores were investigated using Pearson correlation.
We used a one-way repeated measures ANOVA to test whether the average scores of perceived beauty and attractiveness were equivalent. The visual inspection of the model residuals did not show deviations from the assumptions of normality. Through a repeated measures correlation test (rrm), we also investigated the existence of a correlation between perceived beauty and attractiveness scores. For both tests, we included the ID of the participants as a random effect.
We used linear mixed-effects models (LMMs) to test whether beauty and attractiveness scores were affected by the facial asymmetry of the observed face. We included random intercepts and slopes within participants and intercepts within stimuli as random effects. Following Clemente et al. [82], we extracted individual slope estimates from the random-effects structure of LMMs as a measure of aesthetic sensitivity—the extent to which a particular sensory feature affects someone’s aesthetic valuation [92]—to facial asymmetry for each participant. In this case, negative slope values indicate lower preference for facial asymmetry, which is expected for most participants, whereas positive values indicate greater preference for facial asymmetry. Shapiro–Wilk test was used to assess the distributions’ normality of individual slopes. Skewness and kurtosis are shown when the Shapiro–Wilk test p-value < 0.05.
In addition, we used multiple linear regression models to test whether individual aesthetic sensitivity to facial asymmetry is affected by the participants’ art expertise. In all LMMs and multiple linear regression models, fixed effects were previously centered and scaled (to avoid multicollinearity problems). A parametric modeling strategy was used since (i) visual analysis of the residuals revealed approximately normal distributions and (ii) parametric models have been successful in modeling Likert and other numerical rating scales with more than 5 points (7 points in the present study) and with a sample size larger than 30 [93].
The level of significance was set at α = 0.05 for all statistical analyses. The statistical analyses and the respective visualizations were made using the R software version 4.1.2 [94] and the packages lmertest [95], rmcorr [96], effects [97], and ggplot2 [98].

2.6. Ethical Considerations

Before the experiment, participants were informed through the Informed Consent Form about their rights, the topic addressed, and the associated risks and benefits. All methods were performed in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Center for Tropical Medicine from the Federal University of Pará (report #3.220.201/2019).

3. Results

3.1. Differences across Groups in Art Expertise, Creativity, and Art Interest

Although we used art expertise as a continuous variable, we artificially divided the sample into groups of laypersons (n = 48), visual artists (n = 27), dancers (n = 19), and musicians (n = 22) to check if the Arts Expertise Questionnaire was effective in quantifying expertise. Thus, it is expected that the group of experienced artists (students and professionals in the field) obtain higher scores in their respective artistic areas than participants from other groups.
We found an effect of group on visual arts (F(3112) = 36.14, p < 0.001), dance (F(3112) = 53.96, p < 0.001) and music (F(3112) = 35.48, p < 0.001) scores. Visual artists scored higher than laypersons (p < 0.001), musicians (p < 0.001), and dancers (p < 0.001) on the visual arts expertise section. Dancers also scored higher than laypersons in visual arts expertise section (p = 0.035). Dancers scored higher than laypersons (p < 0.001), visual artists (p < 0.001), and musicians (p < 0.001) on the dance expertise section. Furthermore, musicians scored higher than laypersons (p < 0.001), visual artists (p < 0.001), and dancers (p < 0.001) on the music expertise section. The distributions of art expertise scores in the different modalities are shown in Figure 2.
We found an effect of group on the creativity (CQA) scores in the visual arts (χ2(3) = 53.06, p < 0.001), music (χ2(3) = 49.35, p < 0.001), and dance (χ2(3) = 45.18, p < 0.001) domains and on art interest (Part A of VAIAK) (χ2(3) = 28.16, p < 0.001). Visual artists scored higher than laypersons (p < 0.001), musicians (p < 0.001), and dancers (p < 0.001) on the creativity in visual arts domain (Figure 3A). Dancers scored higher than laypersons (p < 0.001), visual artists (p < 0.001), and musicians (p < 0.001) on the creativity in dance domain (Figure 3B). Furthermore, musicians scored higher than laypersons (p < 0.001), visual artists (p < 0.001), and dancers (p < 0.001) on the creativity in music domain (Figure 3C). Laypersons scored less than visual artists (p = 0.001), dancers (p < 0.001), and musicians (p < 0.001) on art interest (Figure 3D). No significant differences for art interest were found between the groups of artists.
The expertise scores of the participants in the three artistic modalities correlated significantly with creativity in their respective domains (visual arts: r = 0.7, p < 0.001; music: r = 0.58, p < 0.001; and dance: r = 0.54, p < 0.001). In addition, art interest correlated significantly with the expertise scores of the participants in all the artistic areas (visual arts: r = 49, p < 0.001; music: r = 0.45, p < 0.001; and dance: r = 0.24, p = 0.009). The relationship between these variables is shown in Figure 3E–H. It is also possible to visualize that visual artists (Figure 3E), musicians (Figure 3F), and dancers (Figure 3G) had higher scores in expertise, art interest, and creativity in their respective domains.

3.2. Beauty and Attractiveness Ratings

Regarding the evaluation of the faces by the participants, the beauty ratings (mean ± SE = 3.45 ± 0.08) were significantly higher than the attractiveness ratings (mean ± SE = 2.54 ± 0.08; F(1, 23187) = 2225.5, p < 0.001; Figure 4A). Moreover, the beauty and attractiveness ratings had a significant correlation (rrm = 0.64, 95% CI [0.62, 0.65], p < 0.001; Figure 4B).

3.3. Influence of Facial Asymmetry on Beauty and Attractiveness Ratings

Facial asymmetry did not significantly influence beauty (β = −0.028, SE = 0.078, p = 0.723) or attractiveness ratings (β = −0.038, SE = 0.068, p = 0.578) (Table 1). Individual slopes extracted from the mixed model of beauty rating range from −0.094 to 0.048 (mean ± SE = −0.028 ± 0.029, Figure 5A). Similarly, individual slopes extracted from the attractiveness rating model range from −0.079 to 0.036 (mean ± SE = −0.038 ± 0.026, Figure 5B). For both models, negative slope values indicate lower preference for facial asymmetry, whereas positive values indicate greater preference for facial asymmetry. Slopes of beauty rating model were normally distributed (W = 0.99, p = 0.945). Slopes of attractiveness rating model were left skewed distributed (W = 0.96, p = 0.004, skewness = 0.52, kurtosis = 2.64). Most participants had negative slopes estimates values for both beauty (82.7%, n = 96) and attractiveness (89.6%, n = 104) rating models, indicating that face asymmetry is poorly rated in both types of evaluation.

3.4. Influence of Art Expertise on Aesthetic Sensitivity to Facial Asymmetry

There was a significant effect of expertise in visual arts (β = 0.006, SE = 0.003, p = 0.034) and dance (β = 0.007, SE = 0.003, p = 0.011), but not for music (β = −0.0002, SE = 0.003, p = 0.565), on aesthetic sensitivity for facial asymmetry in the model based on individual slopes of beauty ratings (Table 2, Figure 6). The variance in aesthetic sensitivity to facial asymmetry decreased with increasing visual arts and dance expertise (Figure 6). The greater the visual arts and dance expertise, the more predominant were individual slope values close to zero, indicating indifference to facial asymmetry. None of the art expertise was significant in the model based on individual slopes of attractiveness ratings (Table 2). That is, people with higher expertise in visual arts and dance tended to disregard facial asymmetry for beauty ratings, but not for attractiveness ratings. The same trend was not found for music experts.

4. Discussion

We investigated for the first time the role of expertise in visual arts, music, and dance, in assessing the beauty and attractiveness of human faces with different asymmetries. Following theoretical models about aesthetic processing, it is expected that art experts and laypersons will differ on their aesthetic evaluation of different sensory features [53,54,55,56,57], including visual symmetry or asymmetry [49,50,51]. Exploring individual differences, we found that people with higher visual arts and dance expertise tend to disregard facial asymmetry in beauty evaluation of human faces, but not in attractiveness evaluation. The same trend was not found for music experts.
In this work, we use a continuous measure for art expertise as proposed by several authors [70,83,99]. Many studies have used art expertise as a quasi-categorical variable, artificially dividing participants into artists and non-artists—a dichotomy that does not capture the variability within groups concerning this variable [70]. As an alternative, we use a questionnaire that takes into account not only formal education but professional experiences, skills, and other artistic experiences. Art experts scored highest in their specific areas (e.g., dancers scored higher than other groups in the dance expertise section of our questionnaire). Moreover, art expertise significantly correlated with art interest and with creativity in their specific domains (e.g., visual art expertise scores correlated significantly with creativity scores in visual arts). Such results provide evidence of the instrument’s validity to measure expertise.
Most research investigating human preference for faces uses the terms “beauty” and “attractiveness” as synonyms, or simply does not differentiate between them [24,100,101,102]. In this study, however, we consider beauty and attractiveness as two different variables, as has been done by some authors [103,104,105]. We observed that although there is a moderate correlation between these two variables, the mean scores for beauty and attractiveness were significantly different. As discussed below, different patterns of individual differences in the assessment of beauty and attractiveness were found, suggesting that they are, in fact, two different variables. In our protocol, after the presentation of the image of the face to be evaluated, the participant first indicated the respective beauty rating, and only afterward the attractiveness rating, so we cannot completely exclude the possibility that the sequence of events may have interfered with the response.
In general, we found that the degree to which facial asymmetry affects beauty evaluation (i.e., aesthetic sensitivity to facial asymmetry) was influenced by participant’s visual arts and dance expertise, but not music expertise. Previous research has found an effect of visual arts expertise in the aesthetic evaluation of symmetry/asymmetry in abstract figures [49,50,51]. The results of Weichselbaum et al. [50] and Gartus et al. [51] indicated that art experts, in general, tended to evaluate stimuli independently from their asymmetry when compared to laypersons. Our results demonstrate that the same trend found for abstract figures can be expected for human faces aesthetic evaluation, not only for visual arts experts but also for dance experts.
While we found a positive effect of the expertise in visual arts and dance on the aesthetic sensitivity to facial asymmetry based on individual differences in the perceived beauty of human faces, we didn’t find the same result for music. Visual and auditory stimuli are evaluated differently, and while the appreciation of visual arts and dance relies on vision, the appreciation of music relies on sound. Thus, the differences in the visual assessment of beauty may be related to the peculiarities of each artistic category. These results are consistent with Clemente et al. [82], who found stimuli modality-specific (visual/auditory) effects on evaluative judgments. Moreover, musicians often have a high affinity to symmetrical features, as these are essential to organize the tempo of a melody [5,106].
No evidence for the effect of any of the three areas of art expertise on aesthetic sensitivity to facial asymmetry based on attractiveness ratings was found. This difference between beauty and attractiveness can be explained by the mate choice importance in our species. The mate choice criteria tend to be more stable during human development [107], and therefore should be less influenced by art training. However, it is necessary to take into account the participant’s sexual orientation and the gender of the person in the photo evaluated to discuss mate choice accurately. Since our experimental design is not suitable for this type of analysis, we also suggest that further studies take into account these variables.
We also found no effect of facial asymmetry on general beauty or attractiveness ratings. Despite several studies showed that facial asymmetry is an important predictor of facial preference, the magnitude of this effect is relatively small based on meta-analytic estimates [29]. It is also possible that this effect was not found in our study since our sample includes art experts, whose beauty assessment tended to disregard facial asymmetry as commented above.
A possible limitation of this study is the under-representation of highly specialized artists in the areas of interest. This happened in our study because we mostly sought out participants in a general university population, and not in art courses and artistic spaces. Therefore, we suggest that new studies should include more participants with higher art expertise.
The present study concludes that people with different art expertise use asymmetry information differently to evaluate facial beauty. This result can be important in understanding how the facial aesthetic evaluation is modified by this type of training and to give us clues about the way symmetry perception can be affected during human development.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/sym14020423/s1, Supplementary file S1: Recruitment of research participants form, Supplementary file S2: Sociodemographic questionnaire, Supplementary file S3: Arts Expertise Questionnaire, and Supplementary Data S1.

Author Contributions

Conceptualization, L.C.P.M., V.E.F.d.N., A.C.d.S. and R.C.R.; Formal Analysis, L.C.P.M., V.E.F.d.N. and A.C.M.; Investigation, L.C.P.M., V.E.F.d.N. and A.C.d.S.; Methodology, L.C.P.M., V.E.F.d.N., A.C.d.S., G.S.S. and R.C.R.; Software, G.S.S.; Supervision, R.C.R.; Visualization, L.C.P.M. and A.C.M.; Writing—original draft, L.C.P.M., V.E.F.d.N. and A.C.d.S.; Writing—review & editing, A.C.M., G.S.S. and R.C.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by research CNPq grant (431748/2016-0). L.C.P.M. received a CAPES scholarship for graduate students. G.S.S. is CNPq Fellows. CNPq Productivity Grant to G.S.S. is 310845/2018-1. CAPES scholarship grant to L.C.P.M. is 88887.497012/2020-00. The funders had no role in study design.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Center for Tropical Medicine from the Federal University of Pará (report #3.220.201/2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available in Supplementary Data S1.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Treder, M.S. Behind the Looking-Glass: A Review on Human Symmetry Perception. Symmetry 2010, 2, 1510–1543. [Google Scholar] [CrossRef]
  2. Bertamini, M.; Rampone, G. The Study of Symmetry in Empirical Aesthetics. In The Oxford Handbook of Empirical Aesthetics; Nadal, M., Vartanian, O., Eds.; Oxford University Press: Oxford, UK, 2020. [Google Scholar]
  3. Hargittai, M.; Hargittai, I. Visual Symmetry; World Scientific Publishing Company: Budapest, Hungary, 2009; ISBN 978-9812835314. [Google Scholar]
  4. Apthorp, D.; Bell, J. Symmetry is less than meets the eye. Curr. Biol. 2015, 25, R267–R268. [Google Scholar] [CrossRef] [Green Version]
  5. Donnini, R. The visualization of music: Symmetry and asymmetry. Comput. Math. Appl. 1986, 12, 435–463. [Google Scholar] [CrossRef] [Green Version]
  6. Clemente, A.; Vila-Vidal, M.; Pearce, M.T.; Aguiló, G.; Corradi, G.; Nadal, M. A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments. Behav. Res. Methods 2020, 52, 1491–1509. [Google Scholar] [CrossRef]
  7. Golomer, E.; Rosey, F.; Dizac, H.; Mertz, C.; Fagard, J. The influence of classical dance training on preferred supporting leg and whole body turning bias. Laterality Asymmetries Body Brain Cogn. 2009, 14, 165–177. [Google Scholar] [CrossRef] [PubMed]
  8. Lindell, A.K. Lateral thinkers are not so laterally minded: Hemispheric asymmetry, interaction, and creativity. Laterality Asymmetries Body Brain Cogn. 2011, 16, 479–498. [Google Scholar] [CrossRef] [PubMed]
  9. Giurfa, M.; Eichmann, B.; Menzel, R. Symmetry perception in an insect. Nature 1996, 382, 458–461. [Google Scholar] [CrossRef]
  10. Møller, A.P.; Sorci, G. Insect preference for symmetrical artificial flowers. Oecologia 1998, 114, 37–42. [Google Scholar] [CrossRef]
  11. Rodríguez, I.; Gumbert, A.; Hempel de Ibarra, N.; Kunze, J.; Giurfa, M. Symmetry is in the eye of the “beeholder”: Innate preference for bilateral symmetry in flower-naïve bumblebees. Naturwissenschaften 2004, 91, 374–377. [Google Scholar] [CrossRef]
  12. Merry, J.W.; Morris, M.R. Preference for symmetry in swordtail fish. Anim. Behav. 2001, 61, 477–479. [Google Scholar] [CrossRef] [Green Version]
  13. Morris, M.R. Female preference for trait symmetry in addition to trait size in swordtail fish. Proc. R. Soc. Lond. Ser. B Biol. Sci. 1998, 265, 907. [Google Scholar] [CrossRef] [Green Version]
  14. Tudor, M.S.; Morris, M.R. Experience Plays a Role in Female Preference for Symmetry in the Swordtail Fish Xiphophorus malinche. Ethology 2009, 115, 812–822. [Google Scholar] [CrossRef]
  15. Clara, E.; Regolin, L.; Vallortigara, G. Preference for symmetry is experience dependent in newborn chicks (Gallus gallus). J. Exp. Psychol. Anim. Behav. Process. 2007, 33, 12–20. [Google Scholar] [CrossRef]
  16. Jansson, L.; Forkman, B.; Enquist, M. Experimental evidence of receiver bias for symmetry. Anim. Behav. 2002, 63, 617–621. [Google Scholar] [CrossRef] [Green Version]
  17. Paukner, A.; Wooddell, L.J.; Lefevre, C.E.; Lonsdorf, E.; Lonsdorf, E. Do capuchin monkeys (Sapajus apella) prefer symmetrical face shapes? J. Comp. Psychol. 2017, 131, 73–77. [Google Scholar] [CrossRef] [PubMed]
  18. Waitt, C.; Little, A.C. Preferences for Symmetry in Conspecific Facial Shape Among Macaca mulatta. Int. J. Primatol. 2006, 27, 133–145. [Google Scholar] [CrossRef]
  19. Che, J.; Sun, X.; Gallardo, V.; Nadal, M. Cross-cultural empirical aesthetics. In The Arts and the Brain: Psychology and Physiology Beyond Pleasure; Christensen, J.F., Gomila, A., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; pp. 77–103. ISBN 9780128139813. [Google Scholar]
  20. Mach, E. Beiträge zur Analyse der Empfindungen (Contributions to the Analysis of Sensations). Rev. Philos. France Let. 1886, 23, 80–83. [Google Scholar]
  21. Höfel, L.; Jacobsen, T. Electrophysiological Indices of Processing Symmetry and Aesthetics. J. Psychophysiol. 2007, 21, 9–21. [Google Scholar] [CrossRef]
  22. Jacobsen, T.; Höfel, L. Descriptive and evaluative judgment processes: Behavioral and electrophysiological indices of processing symmetry and aesthetics. Cogn. Affect. Behav. Neurosci. 2003, 3, 289–299. [Google Scholar] [CrossRef]
  23. Hůla, M.; Flegr, J. What flowers do we like? The influence of shape and color on the rating of flower beauty. PeerJ 2016, 4, e2106. [Google Scholar] [CrossRef] [Green Version]
  24. Rhodes, G.; Proffitt, F.; Grady, J.M.; Sumich, A. Facial symmetry and the perception of beauty. Psychon. Bull. Rev. 1998, 5, 659–669. [Google Scholar] [CrossRef] [Green Version]
  25. Bertamini, M.; Rampone, G.; Makin, A.D.J.; Jessop, A. Symmetry preference in shapes, faces, flowers and landscapes. PeerJ 2019, 7, e7078. [Google Scholar] [CrossRef]
  26. Vessel, E.A.; Maurer, N.; Denker, A.H.; Starr, G.G. Stronger shared taste for natural aesthetic domains than for artifacts of human culture. Cognition 2018, 179, 121–131. [Google Scholar] [CrossRef] [PubMed]
  27. Makin, A.D.J.; Pecchinenda, A.; Bertamini, M. Implicit affective evaluation of visual symmetry. Emotion 2012, 12, 1021–1030. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Watson, P.J.; Thornhill, R. Fluctuating asymmetry and sexual selection. Trends Ecol. Evol. 1994, 9, 21–25. [Google Scholar] [CrossRef]
  29. Rhodes, G. The Evolutionary Psychology of Facial Beauty. Annu. Rev. Psychol. 2006, 57, 199–226. [Google Scholar] [CrossRef] [Green Version]
  30. Little, A.C.; Apicella, C.L.; Marlowe, F.W. Preferences for symmetry in human faces in two cultures: Data from the UK and the Hadza, an isolated group of hunter-gatherers. Proc. R. Soc. B Biol. Sci. 2007, 274, 3113–3117. [Google Scholar] [CrossRef] [Green Version]
  31. Perrett, D.I.; May, K.A.; Yoshikawa, S. Facial shape and judgements of female attractiveness. Nature 1994, 368, 239–242. [Google Scholar] [CrossRef]
  32. Germine, L.; Russell, R.; Bronstad, P.M.; Blokland, G.A.M.; Smoller, J.W.; Kwok, H.; Anthony, S.E.; Nakayama, K.; Rhodes, G.; Wilmer, J.B. Individual Aesthetic Preferences for Faces Are Shaped Mostly by Environments, Not Genes. Curr. Biol. 2015, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Rhodes, G.; Geddes, K.; Jeffery, L.; Dziurawiec, S.; Clark, A. Are Average and Symmetric Faces Attractive to Infants? Discrimination and Looking Preferences. Perception 2002, 31, 315–321. [Google Scholar] [CrossRef] [Green Version]
  34. Saxton, T.K.; Debruine, L.M.; Jones, B.C.; Little, A.C.; Craig Roberts, S. A longitudinal study of adolescents’ judgments of the attractiveness of facial symmetry, averageness and sexual dimorphism. J. Evol. Psychol. 2011, 9, 43–55. [Google Scholar] [CrossRef] [Green Version]
  35. Enquist, M.; Arak, A. Symmetry, beauty and evolution. Nature 1994, 372, 169–172. [Google Scholar] [CrossRef] [PubMed]
  36. Reber, R.; Winkielman, P.; Schwarz, N. Effects of Perceptual Fluency on Affective Judgments. Psychol. Sci. 1998, 9, 45–48. [Google Scholar] [CrossRef] [Green Version]
  37. Reber, R.; Schwarz, N.; Winkielman, P. Processing Fluency and Aesthetic Pleasure: Is Beauty in the Perceiver’s Processing Experience? Pers. Soc. Psychol. Rev. 2004, 8, 364–382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Pound, N.; Lawson, D.W.; Toma, A.M.; Richmond, S.; Zhurov, A.I.; Penton-Voak, I.S. Facial fluctuating asymmetry is not associated with childhood ill-health in a large British cohort study. Proc. R. Soc. B Biol. Sci. 2014, 281, 20141639. [Google Scholar] [CrossRef] [Green Version]
  39. Van Dongen, S.; Gangestad, S.W. Human fluctuating asymmetry in relation to health and quality: A meta-analysis. Evol. Hum. Behav. 2011, 32, 380–398. [Google Scholar] [CrossRef]
  40. Shepherd, K.; Bar, M. Preference for Symmetry: Only on Mars? Perception 2011, 40, 1254–1256. [Google Scholar] [CrossRef] [Green Version]
  41. Humphrey, D. Preferences in Symmetries and Symmetries in Drawings: Asymmetries between Ages and Sexes. Empir. Stud. Arts 1997, 15, 41–60. [Google Scholar] [CrossRef]
  42. Little, A.C.; Jones, B.C.; DeBruine, L.M.; Feinberg, D.R. Symmetry and sexual dimorphism in human faces: Interrelated preferences suggest both signal quality. Behav. Ecol. 2008, 19, 902–908. [Google Scholar] [CrossRef] [Green Version]
  43. Little, A.C.; Jones, B.C.; Burt, D.M.; Perrett, D.I. Preferences for symmetry in faces change across the menstrual cycle. Biol. Psychol. 2007, 76, 209–216. [Google Scholar] [CrossRef]
  44. Møller, A.P.; Thornhill, R. Bilateral Symmetry and Sexual Selection: A Meta-Analysis. Am. Nat. 1998, 151, 174. [Google Scholar] [CrossRef] [PubMed]
  45. Zaidel, D.W.; Deblieck, C. Attractiveness of natural faces compared to computer constructed perfectly symmetrical faces. Int. J. Neurosci. 2007, 117, 423–431. [Google Scholar] [CrossRef] [PubMed]
  46. Swaddle, J.P.; Cuthill, I.C. Asymmetry and human facial attractiveness: Symmetry may not always be beautiful. Proc. R. Soc. London. Ser. B Biol. Sci. 1995, 261, 111–116. [Google Scholar] [CrossRef]
  47. Huang, Y.; Xue, X.; Spelke, E.; Huang, L.; Zheng, W.; Peng, K. The aesthetic preference for symmetry dissociates from early-emerging attention to symmetry. Sci. Rep. 2018, 8, 6263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Vingilis-Jaremko, L.; Maurer, D. The Influence of Symmetry on Children’s Judgments of Facial Attractiveness. Perception 2013, 42, 302–320. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Leder, H.; Tinio, P.P.L.; Brieber, D.; Kröner, T.; Jacobsen, T.; Rosenberg, R. Symmetry Is Not a Universal Law of Beauty. Empir. Stud. Arts 2019, 37, 104–114. [Google Scholar] [CrossRef] [Green Version]
  50. Weichselbaum, H.; Leder, H.; Ansorge, U. Implicit and Explicit Evaluation of Visual Symmetry as a Function of Art Expertise. i-Perception 2018, 9, 204166951876146. [Google Scholar] [CrossRef]
  51. Gartus, A.; Völker, M.; Leder, H. What Experts Appreciate in Patterns: Art Expertise Modulates Preference for Asymmetric and Face-Like Patterns. Symmetry 2020, 12, 707. [Google Scholar] [CrossRef]
  52. Augustin, M.D.; Leder, H. Art expertise: A study of concepts and conceptual spaces. Psychol. Sci. 2006, 48, 135–136. [Google Scholar]
  53. Chatterjee, A.; Vartanian, O. Neuroscience of aesthetics. Ann. N. Y. Acad. Sci. 2016, 1369, 172–194. [Google Scholar] [CrossRef]
  54. Leder, H.; Belke, B.; Oeberst, A.; Augustin, D. A model of aesthetic appreciation and aesthetic judgments. Br. J. Psychol. 2004, 95, 489–508. [Google Scholar] [CrossRef]
  55. Redies, C. Combining universal beauty and cultural context in a unifying model of visual aesthetic experience. Front. Hum. Neurosci. 2015, 9, 218. [Google Scholar] [CrossRef] [Green Version]
  56. Tinio, P.P.L. From artistic creation to aesthetic reception: The mirror model of art. Psychol. Aesthet. Creat. Arts 2013, 7, 265–275. [Google Scholar] [CrossRef]
  57. Leder, H.; Nadal, M. Ten years of a model of aesthetic appreciation and aesthetic judgments: The aesthetic episode—Developments and challenges in empirical aesthetics. Br. J. Psychol. 2014, 105, 443–464. [Google Scholar] [CrossRef]
  58. Hekkert, P.; van Wieringen, P.C.W. The impact of level of expertise on the evaluation of original and altered versions of post-impressionistic paintings. Acta Psychol. 1996, 94, 117–131. [Google Scholar] [CrossRef]
  59. Mullennix, J.W.; Robinet, J. Art Expertise and the Processing of Titled Abstract Art. Perception 2018, 47, 359–378. [Google Scholar] [CrossRef]
  60. Leder, H.; Carbon, C.-C.; Ripsas, A.-L. Entitling art: Influence of title information on understanding and appreciation of paintings. Acta Psychol. 2006, 121, 176–198. [Google Scholar] [CrossRef] [PubMed]
  61. Silvia, P.J. Interested Experts, Confused Novices: Art Expertise and the Knowledge Emotions. Empir. Stud. Arts 2013, 31, 107–115. [Google Scholar] [CrossRef]
  62. Leder, H.; Gerger, G.; Brieber, D.; Schwarz, N. What makes an art expert? Emotion and evaluation in art appreciation. Cogn. Emot. 2014, 28, 1137–1147. [Google Scholar] [CrossRef] [PubMed]
  63. Pihko, E.; Virtanen, A.; Saarinen, V.-M.; Pannasch, S.; Hirvenkari, L.; Tossavainen, T.; Haapala, A.; Hari, R. Experiencing Art: The Influence of Expertise and Painting Abstraction Level. Front. Hum. Neurosci. 2011, 5. [Google Scholar] [CrossRef] [Green Version]
  64. Vogt, S.; Magnussen, S. Expertise in Pictorial Perception: Eye-Movement Patterns and Visual Memory in Artists and Laymen. Perception 2007, 36, 91–100. [Google Scholar] [CrossRef]
  65. Bimler, D.L.; Snellock, M.; Paramei, G.V. Art expertise in construing meaning of representational and abstract artworks. Acta Psychol. 2019, 192, 11–22. [Google Scholar] [CrossRef]
  66. O’hare, D. Individual differences in perceived similarity and preference for visual art: A multidimensional scaling analysis. Percept. Psychophys. 1976, 20, 445–452. [Google Scholar] [CrossRef]
  67. Orr, M.G.; Ohlsson, S. Relationship Between Complexity and Liking as a Function of Expertise. Music Percept. 2005, 22, 583–611. [Google Scholar] [CrossRef]
  68. van Paasschen, J.; Bacci, F.; Melcher, D.P. The Influence of Art Expertise and Training on Emotion and Preference Ratings for Representational and Abstract Artworks. PLoS ONE 2015, 10, e0134241. [Google Scholar] [CrossRef] [Green Version]
  69. Fudali-Czyż, A.; Francuz, P.; Augustynowicz, P. The Effect of Art Expertise on Eye Fixation-Related Potentials During Aesthetic Judgment Task in Focal and Ambient Modes. Front. Psychol. 2018, 9, 1972. [Google Scholar] [CrossRef] [Green Version]
  70. Pang, C.Y.; Nadal, M.; Müller-paul, J.S.; Rosenberg, R.; Klein, C. Electrophysiological correlates of looking at paintings and its association with art expertise. Biol. Psychol. 2013, 93, 246–254. [Google Scholar] [CrossRef]
  71. Altenmüller, E.; Gruhn, W.; Parlitz, D.; Liebert, G. The impact of music education on brain networks: Evidence from EEG-studies. Int. J. Music Educ. 2000, 35, 47–53. [Google Scholar] [CrossRef]
  72. Berkowitz, A.L.; Ansari, D. Expertise-related deactivation of the right temporoparietal junction during musical improvisation. Neuroimage 2010, 49, 712–719. [Google Scholar] [CrossRef] [PubMed]
  73. Burzynska, A.Z.; Finc, K.; Taylor, B.K.; Knecht, A.M.; Kramer, A.F. The Dancing Brain: Structural and Functional Signatures of Expert Dance Training. Front. Hum. Neurosci. 2017, 11, 566. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Calvo-Merino, B.; Glaser, D.E.; Grèzes, J.; Passingham, R.E.; Haggard, P. Action Observation and Acquired Motor Skills: An fMRI Study with Expert Dancers. Cereb. Cortex 2005, 15, 1243–1249. [Google Scholar] [CrossRef] [Green Version]
  75. Lindell, A.K.; Mueller, J. Can science account for taste? Psychological insights into art appreciation. J. Cogn. Psychol. 2011, 23, 453–475. [Google Scholar] [CrossRef]
  76. Müller, M.; Höfel, L.; Brattico, E.; Jacobsen, T. Aesthetic judgments of music in experts and laypersons—An ERP study. Int. J. Psychophysiol. 2010, 76, 40–51. [Google Scholar] [CrossRef]
  77. Smith, J.D.; Melara, R.J. Aesthetic preference and syntactic prototypicality in music: Tis the gift to be simple. Cognition 1990, 34, 279–298. [Google Scholar] [CrossRef]
  78. Bläsing, B.; Calvo-Merino, B.; Cross, E.S.; Jola, C.; Honisch, J.; Stevens, C.J. Neurocognitive control in dance perception and performance. Acta Psychol. 2012, 139, 300–308. [Google Scholar] [CrossRef]
  79. Montero, B. Practice makes perfect: The effect of dance training on the aesthetic judge. Phenomenol. Cogn. Sci. 2012, 11, 59–68. [Google Scholar] [CrossRef]
  80. Marian, V.; Hayakawa, S.; Schroeder, S.R. Cross-Modal Interaction Between Auditory and Visual Input Impacts Memory Retrieval. Front. Neurosci. 2021, 15, 887. [Google Scholar] [CrossRef] [PubMed]
  81. Martino, G.; Marks, L.E. Cross-Modal Interaction between Vision and Touch: The Role of Synesthetic Correspondence. Perception 2000, 29, 745–754. [Google Scholar] [CrossRef] [PubMed]
  82. Clemente, A.; Pearce, M.; Skov, M.; Nadal, M. Evaluative Judgment Across Domains: Liking Balance, Contour, Symmetry and Complexity in Melodies and Visual Designs. Brain Cogn. 2021, 151, 105729. [Google Scholar] [CrossRef]
  83. Specker, E.; Forster, M.; Brinkmann, H.; Boddy, J.; Pelowski, M.; Rosenberg, R.; Leder, H. The Vienna Art Interest and Art Knowledge Questionnaire (VAIAK): A unified and validated measure of art interest and art knowledge. Psychol. Aesthet. Creat. Arts 2018, 12, 172. [Google Scholar] [CrossRef]
  84. Carson, S.H.; Peterson, J.B.; Higgins, D.M. Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire. Creat. Res. J. 2005, 17, 37–50. [Google Scholar] [CrossRef]
  85. Ma, D.S.; Correll, J.; Wittenbrink, B. The Chicago face database: A free stimulus set of faces and norming data. Behav. Res. Methods 2015, 47, 1122–1135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Klingenberg, C. Analyzing Fluctuating Asymmetry with Geometric Morphometrics: Concepts, Methods, and Applications. Symmetry 2015, 7, 843–934. [Google Scholar] [CrossRef] [Green Version]
  87. Küntzler, T.; Höfling, T.T.A.; Alpers, G.W. Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions. Front. Psychol. 2021, 12, 1086. [Google Scholar] [CrossRef] [PubMed]
  88. Sajid, M.; Shafique, T.; Riaz, I.; Imran, M.; Jabbar Aziz Baig, M.; Baig, S.; Manzoor, S. Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity. Symmetry 2018, 10, 232. [Google Scholar] [CrossRef] [Green Version]
  89. Klingenberg, C. MorphoJ: An integrated software package for geometric morphometrics. Mol. Ecol. Resour. 2011, 11, 353–357. [Google Scholar] [CrossRef] [PubMed]
  90. Muñoz-Reyes, J.A.; Pita, M.; Arjona, M.; Sanchez-Pages, S.; Turiegano, E. Who is the fairest of them all? The independent effect of attractive features and self-perceived attractiveness on cooperation among women. Evol. Hum. Behav. 2014, 35, 118–125. [Google Scholar] [CrossRef] [Green Version]
  91. MATLAB; Version 9.4 (R2018a); MathWorks Inc.: Natick, MA, USA, 2018.
  92. Corradi, G.; Chuquichambi, E.G.; Barrada, J.R.; Clemente, A.; Nadal, M. A new conception of visual aesthetic sensitivity. Br. J. Psychol. 2020, 111, 630–658. [Google Scholar] [CrossRef]
  93. Harpe, S.E. How to analyze Likert and other rating scale data. Curr. Pharm. Teach. Learn. 2015, 7, 836–850. [Google Scholar] [CrossRef]
  94. R Core Team. R: A Language and Environment for Statistical Computing; R Core Team: Vienna, Austria, 2019. [Google Scholar]
  95. Kuznetsova, A.; Brockhoff, P.B.; Christensen, R.H.B. lmerTest Package: Tests in Linear Mixed Effects Models. J. Stat. Softw. 2017, 82, 1–26. [Google Scholar] [CrossRef] [Green Version]
  96. Bakdash, J.Z.; Marusich, L.R. Repeated Measures Correlation. Front. Psychol. 2017, 8, 456. [Google Scholar] [CrossRef] [Green Version]
  97. Fox, J.; Weisberg, S. An R Companion to Applied Regression, 3rd ed.; SAGE Publications: Thousand Oaks, CA, USA, 2019. [Google Scholar]
  98. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
  99. Chatterjee, A.; Widick, P.; Sternschein, R.; Smith, W.B.; Bromberger, B. The Assessment of Art Attributes. Empir. Stud. Arts 2010, 28, 207–222. [Google Scholar] [CrossRef]
  100. Chen, A.C.; German, C.; Zaidel, D.W. Brain asymmetry and facial attractiveness: Facial beauty is not simply in the eye of the beholder. Neuropsychologia 1997, 35, 471–476. [Google Scholar] [CrossRef] [Green Version]
  101. Hönekopp, J. Once more: Is beauty in the eye of the beholder? Relative contributions of private and shared taste to judgments of facial attractiveness. J. Exp. Psychol. Hum. Percept. Perform. 2006, 32, 199–209. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Zaidel, D.W.; Aarde, S.M.; Baig, K. Appearance of symmetry, beauty, and health in human faces. Brain Cogn. 2005, 57, 261–263. [Google Scholar] [CrossRef] [Green Version]
  103. Ferdenzi, C.; Delplanque, S.; Vorontsova-Wenger, O.; Pool, E.; Bianchi-Demicheli, F.; Sander, D. Perception of Men’s Beauty and Attractiveness by Women with Low Sexual Desire. J. Sex. Med. 2015, 12, 946–955. [Google Scholar] [CrossRef] [Green Version]
  104. Hayn-Leichsenring, G.U.; Kloth, N.; Schweinberger, S.R.; Redies, C. Adaptation Effects to Attractiveness of Face Photographs and Art Portraits are Domain-Specific. i-Perception 2013, 4, 303–316. [Google Scholar] [CrossRef] [Green Version]
  105. Schulz, K.; Hayn-Leichsenring, G.U. Face Attractiveness versus Artistic Beauty in Art Portraits: A Behavioral Study. Front. Psychol. 2017, 8, 2254. [Google Scholar] [CrossRef] [PubMed]
  106. Wilson, D. Symmetry and its “Love–hate” role in music. In Symmetry; Elsevier: Amsterdam, the Netherlands, 1986; pp. 101–112. [Google Scholar]
  107. Buss, D.M.; Schmitt, D.P. Mate Preferences and Their Behavioral Manifestations. Annu. Rev. Psychol. 2019, 70, 77–110. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. Measurement of facial asymmetry on selected faces (A), distribution of the facial asymmetry scores (B), and experimental procedure (C). In (A) 100 faces were randomly selected from the Chicago Face Database (CFD) [85]. Face++ API was used to automatically add 83 landmarks on each selected face. Data from landmarks underwent a Procrustes superimposition and a Procrustes ANOVA analysis, from which individual facial asymmetry values were extracted. In (B) the full distribution of the facial asymmetry scores is shown. The photos above the histogram are, from left to right, examples of faces with minimum, median and maximum asymmetry scores. In (C) participants responded to the Informed Consent Form, a Sociodemographic Questionnaire, and Arts Expertise Questionnaire. Subsequently, participants performed the face evaluation task. On the first screen, a fixation cross is shown, followed by a face drawn from the CFD, and on the third and fourth screens, respectively, the questions: ‘How beautiful do you think is the face you just saw?’ and ‘How attractive do you think is the face you just saw?’ answered on a 1–7 numerical rating scale.
Figure 1. Measurement of facial asymmetry on selected faces (A), distribution of the facial asymmetry scores (B), and experimental procedure (C). In (A) 100 faces were randomly selected from the Chicago Face Database (CFD) [85]. Face++ API was used to automatically add 83 landmarks on each selected face. Data from landmarks underwent a Procrustes superimposition and a Procrustes ANOVA analysis, from which individual facial asymmetry values were extracted. In (B) the full distribution of the facial asymmetry scores is shown. The photos above the histogram are, from left to right, examples of faces with minimum, median and maximum asymmetry scores. In (C) participants responded to the Informed Consent Form, a Sociodemographic Questionnaire, and Arts Expertise Questionnaire. Subsequently, participants performed the face evaluation task. On the first screen, a fixation cross is shown, followed by a face drawn from the CFD, and on the third and fourth screens, respectively, the questions: ‘How beautiful do you think is the face you just saw?’ and ‘How attractive do you think is the face you just saw?’ answered on a 1–7 numerical rating scale.
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Figure 2. Expertise art score in Visual Arts (A), Dance (B), and Music (C) for groups of laypersons and art experts. The sample was divided according to the professional area or academic background of the participants. Horizontal lines indicate the median, the box shows the interquartile range (IQR), and the whiskers are 1.5× IQR. Each dot beside the boxes represents data from a single participant.
Figure 2. Expertise art score in Visual Arts (A), Dance (B), and Music (C) for groups of laypersons and art experts. The sample was divided according to the professional area or academic background of the participants. Horizontal lines indicate the median, the box shows the interquartile range (IQR), and the whiskers are 1.5× IQR. Each dot beside the boxes represents data from a single participant.
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Figure 3. Creativity, art interest, and the relationship to art expertise scores in visual arts, dance, and music. In (A–D), plots show creativity in visual arts (A), dance (B), and music (C), and art interest (D) as a function of group (laypersons, visual artists, dancers, and musicians). Horizontal lines indicate the median, the box shows the interquartile range (IQR), and the whiskers are 1.5× IQR. In (E–G), plots show the relationship between art expertise scores, art interest, and creativity. The size of the dots represents the creativity score in the respective domain: visual arts (E), dance (F), and music (G). The groups are color coded. In (H), the correlation between art expertise scores and art interest (AI), creativity in visual arts (CVA), dance (CD), and music (CM) domains. The color of tiles is adjusted based on the Pearson correlation coefficient. An X is shown when the correlation coefficient is not significant.
Figure 3. Creativity, art interest, and the relationship to art expertise scores in visual arts, dance, and music. In (A–D), plots show creativity in visual arts (A), dance (B), and music (C), and art interest (D) as a function of group (laypersons, visual artists, dancers, and musicians). Horizontal lines indicate the median, the box shows the interquartile range (IQR), and the whiskers are 1.5× IQR. In (E–G), plots show the relationship between art expertise scores, art interest, and creativity. The size of the dots represents the creativity score in the respective domain: visual arts (E), dance (F), and music (G). The groups are color coded. In (H), the correlation between art expertise scores and art interest (AI), creativity in visual arts (CVA), dance (CD), and music (CM) domains. The color of tiles is adjusted based on the Pearson correlation coefficient. An X is shown when the correlation coefficient is not significant.
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Figure 4. Relationship between beauty and attractiveness ratings. In (A), first panel shows mean ± SE of beauty and attractiveness ratings (partial effects). The second panel shows the mean attractiveness and beauty ratings (colored dots) and the overlapping kernel density estimate of the full distributions of these ratings. Dashed lines indicate the position of means on their respective distributions. In (B) Correlation between beauty and attractiveness (black line) is shown. Gray lines were adjusted separately for each participant using multilevel modeling. In the upper left corner are the correlation coefficient for repeated measures (rrm) and the respective p value.
Figure 4. Relationship between beauty and attractiveness ratings. In (A), first panel shows mean ± SE of beauty and attractiveness ratings (partial effects). The second panel shows the mean attractiveness and beauty ratings (colored dots) and the overlapping kernel density estimate of the full distributions of these ratings. Dashed lines indicate the position of means on their respective distributions. In (B) Correlation between beauty and attractiveness (black line) is shown. Gray lines were adjusted separately for each participant using multilevel modeling. In the upper left corner are the correlation coefficient for repeated measures (rrm) and the respective p value.
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Figure 5. Aesthetic sensitivity to facial asymmetry based on individual slopes extracted from the attractiveness (A) and beauty (B) rating models. Dashed lines indicate zero in x axis. Gray area indicates positive individual slope values.
Figure 5. Aesthetic sensitivity to facial asymmetry based on individual slopes extracted from the attractiveness (A) and beauty (B) rating models. Dashed lines indicate zero in x axis. Gray area indicates positive individual slope values.
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Figure 6. Partial effect of Visual Arts (A) and Dance (B) expertise on aesthetic sensitivity to facial asymmetry based on individual slopes extracted from beauty rating model. Dashed lines indicate zero in y axis. Gray area represents the 95% confidence interval.
Figure 6. Partial effect of Visual Arts (A) and Dance (B) expertise on aesthetic sensitivity to facial asymmetry based on individual slopes extracted from beauty rating model. Dashed lines indicate zero in y axis. Gray area represents the 95% confidence interval.
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Table
Table 1. Estimate (β), standard error (SE) and p values for asymmetry in LMMs for beauty rating and attractiveness rating.
Table 1. Estimate (β), standard error (SE) and p values for asymmetry in LMMs for beauty rating and attractiveness rating.
Beauty RatingAttractiveness Rating
βSEpβSEp
Intercept3.4580.120<0.0012.5080.109<0.001
Facial asymmetry−0.0280.0780.723−0.0380.0680.578
Table
Table 2. Estimate (β), standard error (SE) and p values for Visual Arts, Dance and Music scores in Multiple Linear Regression model for aesthetic sensitivity to facial asymmetry based on individual slopes extracted from the beauty and attractiveness rating models.
Table 2. Estimate (β), standard error (SE) and p values for Visual Arts, Dance and Music scores in Multiple Linear Regression model for aesthetic sensitivity to facial asymmetry based on individual slopes extracted from the beauty and attractiveness rating models.
Beauty RatingAttractiveness Rating
βSEpβSEp
Intercept−0.0280.003<0.001−0.0380.002<0.001
Visual arts0.0060.0030.0340.0020.0020.420
Dance0.0070.0030.0110.0020.0020.354
Music−0.0020.0030.5650.0020.0020.537
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Monteiro, L.C.P.; Nascimento, V.E.F.d.; Carvalho da Silva, A.; Miranda, A.C.; Souza, G.S.; Ripardo, R.C. The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences. Symmetry 2022, 14, 423. https://doi.org/10.3390/sym14020423

AMA Style

Monteiro LCP, Nascimento VEFd, Carvalho da Silva A, Miranda AC, Souza GS, Ripardo RC. The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences. Symmetry. 2022; 14(2):423. https://doi.org/10.3390/sym14020423

Chicago/Turabian Style

Monteiro, Luis Carlos Pereira, Victória Elmira Ferreira do Nascimento, Amanda Carvalho da Silva, Ana Catarina Miranda, Givago Silva Souza, and Rachel Coelho Ripardo. 2022. "The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences" Symmetry 14, no. 2: 423. https://doi.org/10.3390/sym14020423

APA Style

Monteiro, L. C. P., Nascimento, V. E. F. d., Carvalho da Silva, A., Miranda, A. C., Souza, G. S., & Ripardo, R. C. (2022). The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences. Symmetry, 14(2), 423. https://doi.org/10.3390/sym14020423

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