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Scheyd, 2005; Rhodes 2006).to argue that dynamic information was an important

component of overall attractiveness independent of facial

(static) beauty from photographs, although the study could

Evolution and Human BehavKeywords: Facial attractiveness; Dynamic stimuli; Sex differences; Mate choice

1. Introduction

Biological approaches to mate choice hold that cues such

as symmetry (Mller & Thornhill, 1998) and sexual

dimorphism are attractive (Andersson, 1994). In humans,

studies of facial attractiveness have argued that averageness

and symmetry may signal developmental stability and

resistance to disease (Thornhill & Mller, 1997) and

heterozygosity (Gangestand & Buss, 1993) and, hence, be

considered attractive. Sexual dimorphism is attractive in

female faces, perhaps because it signals youth and fertility,

which are valuable traits in a potential mate (Perrett et al.,

1998). These structural cues may signal useful information

to prospective mates (Fink & Penton-Voak, 2002). Sexually

dimorphic characteristics in male faces are not unequivo-

cally attractive but can be in circumstances when putative

heritable bqualityQ is paramount in importance, such as inshort-term as opposed to long-term attractiveness (Little,

Jones, Penton-Voak, Burt, & Perrett, 2002). Two recent

reviews summarise this literature admirably (Gangestad &

Despite the wealth of studies in the area of facial

attractiveness, the general reliance on static stimuli in the

research to date is a concern. Rubenstein (2005) reported a

low correlation between the attractiveness of individual

faces presented in static and dynamic conditions (r=.19

and r=.21, pN .18), a finding that suggests that conclusionsfrom experiments with static faces should be treated with

caution. A static face is, in many ways, a poor stimulus,

since faces are always dynamic in real social interactions.

Facial motion is known to convey cues about identity

(Bassili, 1978; Bruce & Valentine, 1988; Knight &

Johnston, 1997; Lander, Christie, & Bruce, 1999; Pike,

Kemp, Towell, & Phillips, 1997; Thornton & Kourtzi, 2002)

and emotional expression (Bassili, 1978, 1979). Rubenstein

(2005) showed that emotion is an important cue to

attractiveness in dynamic faces, suggesting that static

images may lack important social cues relevant to attrac-

tiveness. Indeed, Riggio, Widaman, Tucker, and Salinas

(1991) used ratings of behaviour from video sequencesFacial movement varies by se

Edward R. Morrisona,4, Lisa GralewskaDepartment of Experimental Psycholog

bDepartment of Computer Science, U

Initial receipt 9 October 2006; f

Abstract

Facial movement has received little attention in studies of human

in many species. This experiment investigated whether facial movem

was associated with attractiveness. We removed shape cues to sex

model. Participants were able to distinguish between male and fem

was a positive association between ease of sex identification and att

suggested several behaviours that are more frequent in women tha

Although some of these behaviours may be cues to sex identificatio

that feminine motion is attractive in female faces, but sexually rec

agreement with work on static faces employing composite images.

D 2007 Elsevier Inc. All rights reserved.1090-5138/$ see front matter D 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.evolhumbehav.2007.01.001

E-mail address: ed.morrison@bris.ac.uk (E.R. Morrison).nd is related to attractiveness

Neill Campbellb, Ian S. Penton-Voaka

versity of Bristol, Bristol BS8 1TU, UK

sity of Bristol, Bristol BS8 1TU, UK

vision received 5 January 2007

ctiveness, yet dynamic displays are an important aspect of courtship

could be used to identify sex, and whether the ease of identification

applying movement from individual faces to a standardised facial

nimations of this model at levels above chance. Furthermore, there

eness for female, but not male, faces. Analysis of facial movement

n (blinking, tilting, nodding, shaking, and amount of movement).

ne alone was directly linked to attractiveness. Our findings suggest

sable movement has no clear influence on male attractiveness, in

ior 28 (2007) 186192not specify what aspects of movement were important in4 Corresponding author. Tel.: +44 0117 954 6621.

these judgements.

independent, allowing estimation of school-level effects.

E.R. Morrison et al. / Evolution and Human Behavior 28 (2007) 186192 187The role of motion in facial attractiveness deserves more

research, and a biological perspective may prove useful: the

importance of dynamic behaviours in mate choice has been

demonstrated in several species. For example, female fruit

flies choose males on the basis of their ability to bdance,Qwhich signals their neuromuscular condition (Maynard-

Smith, 1956). Likewise, male funnel-web spiders that sway

their abdomens at high frequency are more successful at

mating (Singer et al., 2000). Video editing and computer

animation techniques have allowed experimental manipu-

lation of such dynamic behaviours while keeping other cues

constant. For example, Rowland (1995) edited video

footage of male sticklebacks to manipulate the tempo of

their courtship displays and found that females prefer

sequences that were speeded up. Computer animation has

been used to generate artificial sticklebacks in mate

preference experiments (Kunzler & Bakker, 1998). In

humans, motion capture methods have been used to animate

a standard figure in order to study motion while controlling

for variation in static cues. In one recent study, dances by

symmetrical men were rated as most attractive, suggesting

that dynamic cues can signal underlying quality indepen-

dently of static cues, which were constant across all stimuli

(Brown et al., 2005).

Specifying characteristics of movement is problematic,

but sex typicality in facial movement is a potentially

identifiable trait relevant to mate choice: sex-typical traits

are linked to mating success in many species (Andersson,

1994). Some work has investigated sex differences in

human movement. For example, a meta-analysis of studies

using point-light walkers shows that people can recognise

sex from dynamic cues, with accuracy around 70% (Pollick,

Kay, Heim, & Stringer, 2005). However, there are clear

anatomical differences between men and women that

influence gait, especially the wider female pelvis. Anatom-

ical differences on this scale are not present in the face, but,

nonetheless, some evidence suggests that facial movement

of men and women may differ: mens movement may be

more asymmetrical (Alford & Alford, 1981; Alford, 1983),

and womens, more animated (Hall, 1985). A few studies

have tried to present dynamic faces to assess whether sex

can be identified from motion alone. Berry (1991) reported

that adults and children could identify sex from point-light

displays of dynamic faces. However, she was unable to

remove all structural cues to sex (indeed, adults could

identify sex from the static faces, although not as well as in

the dynamic condition). Hill and Johnston (2001) also

explored the role of facial motion on sex identification by

using motion-capture technology to animate an androgy-

nous head with the movement from one of four men or four

women. People could successfully identify sex in this

experiment, and nonrigid motion (movement of the internal

features, rather than larger scale head movement) was

particularly useful for doing so. The same result was found

in similar experiments with point-light animations (Hill,

Jinno, & Johnston, 2003). In these studies, severalThis approach also allows explanatory variables to be added

at either level (e.g., pupils socioeconomic status at level 1

and whether the school was coeducational or mixed at

level 2). In our case, we nested animations (level 1) within

the identity of the actor from which they came (at level 2).

This approach also allowed us to investigate whether any

relationship between sexually identifiable motion and attrac-

tiveness applied across different actors and across different

sequences of movement within actors. Given that dynamic

cues to attractiveness are likely to be ephemeral in nature,

investigating between- and within-target effects is important.

2. Methods

2.1. Facial animations

We obtained facial motion data from 1-min video

sequences for each of five men and five women who were

discussing the same topic (their upcoming Christmas

holidays). The volunteers were all young Caucasian

undergraduate students recruited at Stirling University.

The sequences were labelled semiautomatically with 30

landmark points per video frame (1500 frames in total;

Gralewski, Campbell, Morrison, & Penton-Voak, 2006).

The landmarks were clustered around the mouth and

eyebrows since these areas are the main components of

nonrigid facial motion (Gralewski et al., 2006) and are

important for perception of identity (Hill & Johnston, 2001)

and sex (Bruce & Valentine, 1988). These points were then

used to produce a vector, which provides a shape descriptor

of the face. The face data were scale-normalised to remove

size differences due to varying camera depth.

Since we were interested only in dynamic cues, facial

motion was extracted from the scale-normalised data foranimations were generated from the same actor, meaning,

that the ratings made on them are not strictly independent

and could result in pseudoreplication if the analysis does not

take this lack of independence into account.

To our knowledge, no one has investigated whether facial

movement varies in its attractiveness independently of static

cues. In this experiment, we aimed to use a different method

to replicate the experiment of Hill and Johnston (2001) by

applying male or female movement to the same face model

and also test whether recognisability of the sex of movement

was attractive. We statistically controlled for the lack of

independence in our data using hierarchical linear modelling,

which is a form of regression that does this by explicitly

modelling the variance shared by the units of analysis. In

educational research, for example, pupils from the same

school may have exam scores that are more similar than

pupils from different schools because of the effects of the

school on performance (Bryk & Raudenbush, 1992). A two-

level hierarchical linear model nests pupils (the unit of

analysis at level 1) within schools (at level 2) and, hence,

controls for the fact that pupils exam scores are not all

Each animation was then split into six consecutive 10-s

2.91). The model also revealed that the variation among2

E.R. Morrison et al. / Evolution and Human Behavior 28 (2007) 186192188clips, resulting in a total of 60 clips. The study was

approved by the ethics committee of the Department of

Experimental Psychology, University of Bristol.

We analysed the results using a two-level hierarchical

linear model (using HLM 6.02). This model can be

understood as linear regression looking at the effect of the

sex of motion on the rated sex of each clip, controlling for

the lack of independence between movement sequences

from the same actor. For the judgments about sex, the

outcome variable was the rated sex of each clip, which was

analysed as a binomial variable with 108 trials (i.e., number

of times each stimulus was rated male out of 108). The

level 1 predictor was sex of the movement in each clip,

which was uncentered as it was dummy-coded as 0 for

female and 1 for male. Level 2 units were the identities of

the 10 people whose movement was used, and there were no

level 2 predictors. The level 2 intercept term was treated as a

random effect (because the 10 actors represent only a

sample of all people), whereas the slope term was a fixed

effect (because male and female are the only categories into

which the predictor can fall). This type of analysis was

therefore a random coefficients model, in which the

relationship of interest lay only at level 1. The presence of

units at level 2 was merely to statistically control for the

nonindependence of stimuli derived from the same actor.

Consequently, the degrees of freedom are derived from the

number of units of analysis (60), although they are not

independent, because their shared variance has been taken

into account.

For the attractiveness ratings, the same random coef-

ficients model was used, but now, attractiveness was the

normally distributed continuous outcome variable. The

level 1 predictor was the number of times each clip was

rated male from the first experiment, treated as a random

effect. This model allows testing of two different issues:

whether those individuals whose sex is more identifiable

seem more attractive and whether movement from a

particular individual that is sexually identifiable is also

attractive. Two models were runone for female faces and

one for male. This model can be understood as linear

regression looking at the effect of ease of sex identification

on the attractiveness of each clip, controlling for the lack

of independence.

3. Sex identification

3.1. Participants and procedures

Sixty-eight female (mean age=22.6, S.D. 6.5 years,

range=1853) female and 40 male (mean age=27.1,each individual and used to animate an androgynous line

face, which was generated as the average of the 10 male and

female faces in the videos, thus removing the structural cues

to sexual dimorphism. This produced 10 1-min animations

with motion specific to each individual (see Appendix A).level 2 units was significant [v (9)=156.6, pb .0001],meaning that clips from different actors varied in their

mean ratings. This result suggests that analyses that fail to

control for nonindependence of multiple stimuli derived

from one target are confounded.

4. Attractiveness

4.1. Participants and procedures

For this task, the same clips were blocked by sex, and

participants were informed of the sex of the faces. Con-

sequently, a different set of 28 women (mean age=20.1,

S.D.=1.8 years, range=1828) and eight men (mean

age=20.7, S.D.=3.0 years, range=1827; age of rater had

no effect in any analyses) viewed the clips in random order

within block. The reason for this design, as the first

experiment showed, was that people were not very accurate

at identifying sex (see Section 3.2). This design prevents

possible misclassifications of sex influencing attractiveness

judgements. If the criteria for attractiveness differ across

male and female dynamic stimuli (as they do across male

and female static stimuli), then misclassification of sex (e.g.,

erroneously thinking a male clip were female) would lead to

potentially uninterpretable attractiveness judgements. Be-

cause participants were informed of sex, they could judge

attractiveness in the context of rating opposite sex or same

sex faces. Participants rated the faces for attractiveness on a

scale of 1 (very unattractive) to 7 (very attractive). Interrater

reliability for attractiveness ratings was high (Cronbachs

a=0.84, n=108; for men a=0.68, n=8; for women a=0.86,S.D.=6.3 years, range=1840) participants took part in

the sex identification experiment. Stimuli were presented in

random order using Eprime experimental software. Partic-

ipants were instructed to judge whether each animation was

male or female after viewing it once. Participants responded

using the keyboard and did not receive feedback. Partic-

ipants were not told how the stimuli had been created until

they had finished the experiment.

3.2. Results

Mean accuracy was 57% (S.D.=0.5%), with no differ-

ence between male and female raters. Sex of target,

however, did influence accuracy, which was significantly

higher for male (one-sample t test vs. 50%: mean=61%,

SD=0.5%, t108=11.1, pb .0001) than for female (one-sample t test vs. 50%: mean=54%, SD=0.5%, t108=4.3,

pb .0001) clips (paired-samples t test for the two groups:mean difference=7%, t108=5.7, pb .0001). These effects didnot interact with participant sex (i.e., men were no better or

worse than women at identifying male or female clips).

Male and female stimuli differed in the number of times

they were categorized as male (multilevel regression,

c=0.60, t58=2.62; p=.012). Male stimuli were 1.8 times aslikely to be judged male as female stimuli (95% CI=1.15

n=28). Most participants were undergraduate students who

received course credit for participation.

4.2. Results

In this analysis, we operationalized the sex typicality of a

given stimulus as the number of times that the sex of the

given clip had been correctly identified by the participants

in the first task detailed above (i.e., a clip that was correctly

identified in 100/108 trials is considered to be higher in sex

typicality than a stimulus identified correctly on 70/108

occasions). For female faces, sex typicality defined in this

way predicted attractiveness (multilevel regression, c=1.02,t4=7.08, pb .0001). There was significant variation in theerror term for the level 2 intercept [v2(4)=15.9, p=.004],but not for the slope [v2(4)=3.7, pN .500], meaning thatdifferent actors varied in their mean attractiveness, but

correct identification of sex was associated with attractive-

ness in the same way across actors.

For male faces, sex typicality had no effect on

attractiveness ratings (c=0.31, t4=1.60, p=.184). Nei-ther source of level 2 variation was significant [intercept

v2(4)=5.9, p=.204; slope v2(4)=6.2, p=.181]. Fig. 1 shows

58

E.R. Morrison et al. / Evolution and Human Behavior 28 (2007) 186192 189Fig. 1. The relation between attractiveness and correct sex recognition of

movement for female (upper) and male (lower) faces. Data are z-standardised,

and each symbol represents one actor.between level 2 units was significant for these two

variables. In other words, those clips that blinked and

moved more were more likely to be rated female. Thisthe scatter plot of attractiveness and rated sex typicality for

each level 2 identity of both sexes.

5. Movement analysis

We further investigated which specific aspects of

movement might differ between men and women and,

hence, be potential cues to sex-typical motion and/or

attractiveness. To simplify the task of defining facial

movement (which, like all biological motion, has complex

spatial and temporal properties), we concentrated on five

aspects of movement that could be objectively measured:

blinks, bshakes,Q bnods,Q btilts,Q and total movement. Blinkswere defined simply as number of blinks made during each

sequence. bShakes,Q bnods,Q and total movement weredefined objectively by measuring the movement made by

the centroid (the imaginary average vector position of all

landmark points) in each animation. Graphs of these

movements over time were smoothed (filtered) and the

number of turning points counted. A shake was defined as a

movement that exceeded a left and right threshold3.8% of

the total range of horizontal movement across all individ-

uals. Likewise, a nod was defined as movement that

exceeded an upper and lower threshold8.0% of the total

range of vertical movement. A tilt was defined as movement

of the line between the nose and forehead landmarks that

exceeded two angle thresholds (9.7% of the total range), and

total movement was simply the distance moved by the

centroid from frame to frame. These particular thresholds

were used to strike a balance between every slight

movement becoming a nod or tilt and allowing a reasonable

and realistic number of such movements to be recorded. For

example, the rate of nodding was 3.6 times every 10 s for

women and 2.0 for men; the respective rates of tilting were

2.6 and 1.4.

Each of these behaviours was more common in

women than men when entered as a normally distributed

outcome variable into a hierarchical linear model as

used in Section 3.2. Blinks (c=1.9, t58=25, p=.019),shakes (c=1.38, t58=2.4, p =.02), nods (c=1.7,t58=2.1, p=.039), tilts (c=0.11, t58=4.1, p=.003),and total movement (c=1.19, t58=2.9, p=.006) were allpredicted by sex (female was dummy-coded as 0 and male

as 1, hence the negative coefficients). Variation among the

level 2 units was significant for all five variables, again

suggesting that simple linear regression would be inappro-

priate. We tested the possibility that these cues could be

used to identify sex by entering each variable as a predictor

of the number of times each clip was classified as male by

participants in the sex identification task. Two variables

predicted number of times rated male: blinks (c=0.14,t58 =2.4, p = .04) and total movement (c=0.23,t =2.5, p=.03), whereas the others did not. Variation

interpretation of our findings could be that since the stimuli

E.R. Morrison et al. / Evolution and Human Behavior 28 (2007) 186192190lacked variation in all cues except motion, attractiveness

judgments are driven by sex identification as the only source

of variation, rather than as an ecologically meaningful cue to

attractiveness. The lack of such a relationship in male faces,

however, does not support this interpretation.

Our findings are strikingly similar to those from

studies of static faces, in which femininity is attractive in

female faces (Perrett, May, & Yoshikawa, 1994; Perrett

et al., 1998), but the relationship between masculinity

and attractiveness in male faces is less simple, at least with

composite faces (Rhodes, 2006). Although Rubenstein

(2005) argues that what is attractive in static faces is not

necessarily attractive in dynamic faces, we suggest that at

least one important principle is the same: sexual dimorphism

in static faces and sex typicality in dynamic faces are

attractive (for females).relationship held when sex was included in the model.

Adding the numbers of nods, shakes, and tilts resulted in a

composite variable, bfacial movements,Q which was close topredicting number of times rated male (c=0.04,t58=2.1, p=.06) but not attractiveness when controllingfor blinking and sex.

Odds ratios indicate that the male clips blinked 87%

(95% CI 7799%) as much as female clips and moved 79%

(6497%) as much. None of the five aspects of movement

predicted attractiveness ratings. When the same analyses

were run separately for each sex, the only significant

relationship was that total movement predicted number of

times females were rated male (c=0.30, t58=2.9,p=.046), i.e., female clips that moved more were more

likely to be identified as female.

6. Discussion

These data suggest that men and women differ in the

way they move their faces, and that people are sensitive to

these differences. Furthermore, movement that was more

often identified as female was considered more attractive

in female faces, but no such effect was evident for male

faces. The task was by no means easy, however, as

reflected in the fairly low overall accuracy, and informally,

many participants reported finding the task difficult. Our

value of 57% is quite close to the accuracies of 68% in

Berry (1991) and about 60% in Hill and Johnston (2001),

despite differences in the stimuli and presentation details.

Male animations were more easily identified than female

animations, although accuracy for both was above chance.

Overall, there was a slight tendency for participants to rate

a face as male (53%).

Although the line-drawn stimuli here are evidently not

naturalistic, raters seem able to rate them for attractiveness

in a meaningful way. Interrater reliability in attractiveness

judgements was as high as most studies of photographic

stimuli (although see Honekopp, 2006, for a discussion of

interrater reliability in attractiveness studies). A simpleIn studies with static faces, masculine faces are often

perceived as signalling negative personality traits such as

coldness and dishonesty (Penton-Voak et al., 2001). Should

these personality attributions extend to dynamic properties of

faces, it may, in part, explain why sex typicality is not

attractive in our dynamic clips. Work with composite

computer graphic images has shown that womens prefer-

ences also vary over the menstrual cycle, being higher for

masculine male faces during the fertile phase (Jones et al.,

2005; Penton-Voak et al., 1999; Penton-Voak & Perrett,

2000). Preferences also vary according to mating context,

masculine faces being more attractive as short-term or extra-

pair partners (Perrett et al., 1998), and according to individual

traits such as self-perceived attractiveness (Little, Burt,

Penton-Voak, & Perrett, 2001). Womens preferences may

therefore represent a strategy to trade off the benefits of good

genes signalled by a masculine face with the benefits of a

prosocial partner signalled by a less masculine face. Although

data about the role of masculine facial shapes in real faces

sometimes conflict with findings from these computer

graphic studies (Rhodes, 2006), work in other modalities

and using other techniques indicates that masculinity is

not unequivocally preferred by women in all situations

(Gangestad, Simpson, Cousins, Garver-Apgar, & Christensen,

2004; Roney, Hanson, Durante, & Maestripieri, 2006).

Dynamic facial cues may be particularly important in

investigating these tradeoffs because of the potential for

more information to be conveyed. For example, facial

motion is important in personality attribution as it improves

accuracy over static cues alone (Borkenau & Liebler, 1992;

Watson, 1989) and carries particular information about

emotion (Bente, Feist, & Elder, 1996). Dynamic information

may also signal important aspects of an individuals

underlying biology, which may be relevant to attractiveness.

Static facial masculinity is related to current levels of

circulating testosterone in men (Penton-Voak & Chen,

2004), while static facial femininity is related to current

levels of circulating oestrogen in women (Law Smith et al.,

2005). Body movement may signal symmetry and hormonal

profiles in women (Brown et al., 2005; Grammer, Filova, &

Fieder, 1997; Grammer, Fink, Mller, & Thornhill, 2003).

Because people can change the way they move their faces,

whereas static cues are invariant (at least over the short

term), more temporally precise information can be signalled.

For example, men and women might be expected to alter

their facial movement in different social situations, such as

when interacting with an attractive member of the opposite

sex compared with a rival of the same sex. Our results

demonstrate a relationship between attractiveness and sex

identification both between (some individuals move more

attractively than others across episodes) and within targets

(the attractiveness of a given individual depends on transient

dynamic cues) and support the possibility of these strategic

mate choice behaviours.

Our preliminary analysis of specific aspects of facial

motion identified several sex differences. Blinking, nodding,

perhaps because a more animated face signals extraversion,

ODonohue and Crouch (1996), for example, also showed

to understanding facial perception gave way to configural

E.R. Morrison et al. / Evolution and Human Behavior 28 (2007) 186192 191approaches (e.g., Tanaka & Farah, 1993). In many ways, the

technique we applied here (based on counting of discrete

behaviours) is more similar in concept to a feature-based

approach than a configural approach. More sophisticated

analysis of the spatial and temporal aspects of facial motion

may be necessary to define the properties of movement that

are associated with attractiveness ratings.

Computer animation techniques allow experimental

manipulation of the multiple signals in static and dynamic

human facial cues independently. Similar techniques have

proved fruitful in teasing apart the separate elements of

animal signals in studies of species as diverse as spiders

(Uetz & Roberts, 2002), sticklebacks (Kunzler & Bakker,

2001), and Jacky dragons (Johnstone, 2006; Ord, Peters,

Evans, & Taylor, 2002). The work reported here represents a

first step towards understanding dynamic cues to attractive-

ness in human faces.

Acknowledgments

The authors thank Helen Chang for collecting the raw

video footage used in this study. EM is supported by an

ESRC PhD studentship.

Appendix A. Supplementary data

Supplementary data associated with this article can

be found in the online version at www.doi:10.1016/

j.evolhumbehav.2007.01.001.

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Facial movement varies by sex and is related to attractivenessIntroductionMethodsFacial animations

Sex identificationParticipants and proceduresResults

AttractivenessParticipants and proceduresResults

Movement analysisDiscussionAcknowledgmentsSupplementary dataReferences