Allometry of Facial Mobility in Anthropoid Primates: Implications for the Evolution of
Facial Expression
Seth D. Dobson
Department of Anthropology, Dartmouth College, Hanover, NH 03755
Text pages, including citations: 34
Figures: 4
Tables: 6
Abbreviated title: BODY SIZE AND FACIAL MOBILITY
KEY WORDS: facial movement; constraint; perception; monkeys; apes
Correspondence to: Seth Dobson, Dartmouth College, Department of Anthropology, HB 6047,
Hanover, NH 03755, USA. Tel: (603) 646-3436, Fax: (603) 646-1140, Email:
[email protected]
Grant sponsorship: National Science Foundation, Dissertation Improvement Grant (BCS
0424160); Sigma Xi, Grant-in-Aid of Research
THIS IS A PREPRINT OF AN ARTICLE ACCEPTED FOR PUBLICATION IN AMERICAN JOURNAL OF PHYSICAL
ANTHROPOLOGY COPYRIGHT 2008 WILEY-LISS, INC.
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ABSTRACT
Body size may be an important factor influencing the evolution of facial expression in
anthropoid primates due to allometric constraints on the perception of facial movements. Given
this hypothesis, I tested the prediction that observed facial mobility is positively correlated with
body size in a comparative sample of nonhuman anthropoids. Facial mobility, or the variety of
facial movements a species can produce, was estimated using a novel application of the Facial
Action Coding System (FACS). I used FACS to estimate facial mobility in 12 nonhuman
anthropoid species, based on video recordings of facial activity in zoo animals. Body mass data
were taken from the literature. I used phylogenetic generalized least squares to perform a
multiple regression analysis with facial mobility as the dependent variable and two independent
variables: log body mass and dummy-coded infraorder. Together, body mass and infraorder
explain 92% of the variance in facial mobility. However, the partial effect of body mass is much
stronger than for infraorder. The results of my study suggest that allometry is an important
constraint on the evolution of facial mobility, which may limit the complexity of facial
expression in smaller species. More work is needed to clarify the perceptual bases of this
allometric pattern.
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Anthropoid primates frequently use facial displays to mediate close-range social
interactions between group members (Chevalier-Skolnikoff, 1973; van Hooff, 1962, 1967;
Redican, 1975, 1982). Facial displays provide information about what an individual is likely to
do next (Andrew, 1963a,b, 1964). As such, they are typically used to manage conflicts and
facilitate bonding within groups (Parr et al., 2005; Waller and Dunbar, 2005; Flack and de Waal,
2007). For example, the silent bared-teeth display is a submissive signal that serves an appeasing
function in some species because it communicates the intent to withdraw from an agonistic
encounter (Preuschoft and van Hooff, 1997). This suggests that the evolution of facial
expression, as a system of communication, is linked to the need for group cohesion (Maestripieri,
1999). Despite this universal requirement of all primate social groups, there is considerable
diversity among anthropoids with regard to facial expression.
The first major difference is that platyrrhines tend to rely on facial displays much less
than catarrhines (Redican, 1975). Within platyrrhines, there are also marked differences between
the “poker-faced” members of Callitrichinae and the more visually-expressive Atelinae
(Moynihan, 1967). With regard to cercopithecoids, guenons (Cercopithecus) exhibit fewer and
less-elaborate facial displays than macaques and baboons (Gautier and Gautier-Hion, 1977; van
Hooff, 1962, 1967). Hominoids exhibit considerable diversity in the pattern of facial expression
as well. For example, hylobatids use facial displays less than any other ape and are less visually
expressive than some cercopithecoids (Liebal et al., 2004). On the other hand, common
chimpanzees have one of the largest and most-diverse facial display repertoires of any primate
(van Hooff, 1973; Parr et al., 2005). The causes of this diversity remain to be explained.
One potential explanation for interspecific variation in facial expression is body size.
Allometry, or the study of size and its consequences (Gould, 1966), is a fundamental part of
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comparative biology (Harvey and Pagel, 1991). It is widely known that allometric effects are
important causes of variation in all biological systems (Calder, 1984; Jungers, 1985; Peters,
1983; Schmidt-Nielsen, 1984). Thus, it is reasonable to hypothesize that this is also the case for
facial expression. Moynihan (1967) was the first to suggest that size matters when it comes to
facial expression. He argued that small size is a constraint on the adaptive evolution of facial
expression because facial movements are more difficult to discern in smaller species than in
larger species.
The hypothesis that size limits the perception of facial movements is supported by a large
body of empirical research on visual processing in vertebrates. Visual acuity, or the resolving
power of the eye, is positively correlated with body size (Kiltie, 2000). In other words, largerbodied species are better at processing the fine spatial details of visual stimuli than are smaller
species. This is due in part to larger-bodied species having larger eyes (Walls, 1942; Ross and
Kirk, 2007). Absolute eye size determines the distance between the lens and retina, or posterior
nodal distance, which determines retinal image size (Ross, 2000). The size of the retinal image is
one of several factors underlying visual acuity in the vertebrate eye (Kay and Kirk, 2000). This
explains why Homo sapiens, which has the largest eye diameter of any primate at 24 mm, also
has the highest visual acuity (Kirk and Kay, 2004). Therefore, the ability to produce a wide
variety of facial movements is unlikely to be adaptive at small sizes, because there are sensory
biases against complex facial expression in species with absolutely small eyes.
Given these observations, I hypothesize that smaller species have limited facial mobility
due to allometric constraints on the perception of facial movements. One important prediction of
this hypothesis is that observed facial mobility and body size are positively correlated. The goals
of my paper are to : (1) describe an approach for estimating observed facial mobility in
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anthropoid primates, (2) present new data on facial mobility in a sample of anthropoids, and (3)
present a preliminary test of the hypothesis that body size is positively correlated with facial
mobility in anthropoids. Implications for the evolution of facial expression as a system of
communication will be discussed.
MATERIALS AND METHODS
Coding system
One way to estimate facial mobility for comparative purposes is to count the number of
observable facial movements a species can produce. This can be done using an application of the
Facial Action Coding System (FACS). FACS was developed more than 30 years ago (Ekman
and Friesen, 1976) for describing human facial displays with regard to their muscular basis. It is
the method of choice in psychology for the observational study of facial expression (Ekman and
Rosenberg, 2005). The current version of FACS (Ekman et al., 2002a,b) defines 24 action units
(AUs) that correspond to visible changes in the face resulting from the contraction of individual
muscles or parts of muscles acting separately or in combination. Seventeen other actions are also
described, but their muscular bases are less well known. Simply put, FACS is a detailed
inventory of the observable consequences of all possible facial muscle contractions in humans.
The functional basis of FACS has recently been validated using intramuscular electrical
stimulation (Waller et al., 2006). Action units can be coded from video recordings of facial
activity using standardized criteria with ~80% inter-observer reliability on average (Ekman,
1982).
The logic of FACS can be illustrated with regard to the human expression of fear (Fig. 1).
This display would be coded as AU 1+2+4+5+20 using FACS (Ekman and Friesen, 2003).
Action units 1 and 2 refer to actions that elevate the brow. Action unit 1 (Inner Brow Raiser) is
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caused by the action of the medial portion of frontalis, while AU 2 (Outer Brow Raiser) is
associated with the lateral part of the same muscle. Action unit 4 (Brow Lowerer) pulls the
medial part of the brows down and together due to the convergent action of the procerus,
corrugator supercilii, and depressor supercilii muscles, (or possibly only depressor supercilii,
Waller et al., 2006). Action unit 5 (Upper Lid Raiser) is associated with levator palpebrae
superioris, which lifts the upper eyelid. The shape of the mouth during human fear results from
AU 20 (Lip Stretcher), which is due to the action of the risorius muscle pulling the lip corners
laterally. Waller et al. (2006) provide a more complete analysis and review of the muscular basis
for FACS.
The validity of applying FACS to nonhuman anthropoids has recently been established.
The Chimpanzee Facial Action Coding System, or ChimpFACS, is a modified version of FACS
for Pan troglodytes (Vick et al., 2007). It was developed using careful dissections of chimpanzee
muscles of facial expression (Burrows et al., 2006), systematic identification of chimpanzee
muscle actions using electrical stimulation (Waller et al., 2006), detailed comparisons of coding
criteria between FACS and ChimpFACS (Vick et al., 2007), and quantitative validation of
ChimpFACS with regard to chimpanzee facial displays (Parr et al. 2007). Waller et al. (2006)
demonstrated that electrical stimulations of the same muscles in Pan troglodytes and Homo
sapiens result in similar appearance changes, although humans exhibit a greater number of
potential movements overall. Moreover, the coding criteria defined by ChimpFACS are not
radically different from the human-based FACS, but are more specific to chimpanzees (Vick et
al., 2007). Therefore, these observations imply that FACS can be applied to additional nonhuman
anthropoid species.
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My use of FACS to estimate facial mobility in nonhuman anthropoids is a novel
application of this well-established methodology. The system was designed to objectively
distinguish facial displays based on their structure, rather than meaning (Ekman and Friesen,
1976). This goal was achieved by defining all possible human AUs without regard to facial
expression. In other words, the system is based on functional anatomy, not communication. One
consequence of this approach is that FACS can be used to estimate facial mobility, with or
without regard to facial expression, precisely because it is an inventory of facial muscle
contractions and nothing more. While most applications of FACS focus on direct studies of
facial displays (Ekman and Rosenberg, 2005; Parr et al., 2007), this does not preclude its use for
other purposes.
Comparative sample
The data for this study derive from digital video recordings of 41 captive adult
individuals representing 12 nonhuman anthropoid species from three superfamilies (Table 1).
The animals were located at the St. Louis Zoo (St. Louis, MO) and Lincoln Park Zoo (Chicago,
IL). I collected video recordings over 105 days between May 2004 and May 2005 from the
visitor areas for each species, with the exception of Pan troglodytes. Recordings for this species
were taken from a research group not on display. All enclosures were indoors with glass
partitions between the animals and visitors. Some species also had access to connecting, outdoor
enclosures. All species were fed in the morning. None of the animal-care protocols were altered
for the purposes of this study.
Sample sizes were constrained by the number of adults in each group, which is typically
low for zoo animals ( 5). To maintain comparability across species, I set an upper limit of five
for all species. When more than five adults were present in a group, I chose subjects based on
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physical distinctiveness, to facilitate recording the same individuals over multiple days. In
addition, I made an effort to record both males and females whenever possible. The keepers and
curators provided information on the age, sex, and physical characteristics of individuals.
Video recording
Given the comparative goals of this study, it was necessary to record animals only during
periods of high facial activity, so that I could collect a large amount of comparable data for each
species in a relatively short period of time. It was not feasible to limit data collection to one
specific type of behavior, such as facial displays. Preliminary observations of Ateles geoffroyi
and Papio hamadryas indicated that the best time to record was during morning feeding. I was
able to observe approximately three facial movements per minute on average during feeding,
both within and between masticatory cycles. Moreover, recording muscle actions during morning
feeding in each species normalized the data for context and time of day. The variety of behaviors
recorded related to ingestion, mastication, communication (vocal and visual), as well as facial
movements with no obvious function (facial ticks).
I made close-up recordings of the face using a Sony© Hi8 camcorder (DCR-TRV350)
with Sony© Digital8 tapes (P6-120HMPL). Ten-minute focal samples (Altmann, 1974) were
collected for subjects that fed continuously, in combination with opportunistic sampling for
species in which individuals fed more intermittently or when focal animals moved out of view of
the camera. My goal was to obtain recordings of continuous facial activity in each 10-minute
sample, with only minor fluctuations in activity level. I recorded the subjects over multiple days
for a total of 2 hrs in each species. The order of focal sampling was determined using a random
number generator, which mitigated the over-representation of any single individual. However,
the amount of video was not necessarily equal for all subjects.
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Preliminary observations suggested that 2 hrs of video (12 samples) collected during
feeding time would be more than enough to estimate facial mobility at the species level. This is
verified by plotting the number of new observations as a function of time for each species (Fig.
2). This approach does not preclude the possibility of missing extremely rare AUs. Vick et al.
(2007) based their estimate of the facial action repertoire of Pan troglodytes on 40 hrs of video
footage. However, collecting 40 hrs of video for each species was not feasible in this
comparative study due to constraints on time and resources.
Estimating facial mobility
Choice of action units. I defined facial mobility as the number of facial action units
observed within a species. Estimates of facial mobility were based on a subset of human FACS
(Table 2). For my study, I only examined AUs known to be associated with muscles derived
from the mandibular and infraorbital laminae (Gasser, 1967). These are the embryonic precursors
that give rise to the superficial muscles surrounding the mouth, nose, eyes, and forehead
(Burrows, 2008). I focused on these laminae to enhance comparability across species. The 16
AUs listed in Table 2 represent a small set of actions with visually-similar consequences across a
broad range of species.
There were several AUs that I chose not to code a priori. Action unit 5 (Upper Lid
Raiser), AU 25 (Lips Part), AU 26 (Jaw Drop), and AU 27 (Mouth Stretch) were excluded
because they did not meet the aforementioned developmental criteria. That is, they are not
derivatives of the relevant laminae. Because AU 6 (Cheek Raiser) and AU 7 (Lid Tightener) are
involved in forcefully closing the eyes to protect them from damage, I assumed that they were
present in all species in my sample and did not code them. FACS further defines supplementary
AUs related to eye closure, which were not coded. Action unit 11 (Nasolabial Furrow Deepener)
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was excluded because the criteria for coding this action are too specific to humans, since many
nonhuman anthropoids do not possess a distinct nasolabial furrow (Waller et al., 2006; Vick et
al., 2007). Finally, FACS defines 14 miscellaneous AUs, which I excluded from my analysis due
to their unspecified muscular bases and/or relatively imprecise definitions (Ekman et al., 2002a).
It should be noted that some of the AUs excluded from my study are undoubtedly
important to functions such as facial expression and feeding. Given these exclusions, my
estimates should not be viewed as comprehensive inventories of facial muscle actions for each
species, but as quantitative estimates of a behavioral variable, facial mobility. In addition, I did
not code any ear movements because FACS does not provide coding criteria for these actions.
However, ear movements are components of particular facial displays in some species (van
Hooff, 1962, 1967).
Coding procedure. I examined video recordings with the digital camcorder connected to
a Sony© Trinitron color monitor (PVM-9L1) via a standard S-Video cable. I viewed each sample
twice in slow motion, once each for the upper and lower face. Whenever I saw one of the AUs
listed in Table 2, I would rewind the tape and use frame-by-frame playback to assess whether the
AU occurred, based on standardized FACS criteria (Ekman et al., 2002a). Only behavioral
events with minimal interference from food items, either inside or outside the mouth, were
coded. I estimated facial mobility by adding up the number of different AUs observed for each
species. For an AU to be included, I had to observe it at least four times. Once this threshold was
reached for a particular AU, I did not code it in subsequent samples for that species. This
allowed me to first identify the most common AUs, and then focus my attention on less common
AUs or those that are more difficult to code.
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I examined the video samples for each species one species at a time rather than piecemeal. This was done to develop a mental template of the static facial features of each species.
This template aided visual attention and focus during coding. As each species estimate was
established, new information hastened AU coding in subsequent species. In other words, there
was a steep learning curve associated with applying FACS to nonhuman anthropoids. However, I
made re-evaluations of previous observations in light of other species. Thus, potential biases due
to preconceived expectations were mitigated by constant re-assessment of previous observations.
Given species differences in facial structure, some appearance changes are more useful
than others when coding AUs in nonhuman anthropoids (Vick et al., 2007). My application of
FACS represents a subjective assessment of the similarity of nonhuman anthropoid muscle
actions to the criteria defined by FACS. I did not attempt to validate these similarities based on
the underlying musculature (Waller et al., 2006). Therefore, no claims regarding homology of
these movements can be made. Table 3 describes the most valuable criteria for coding the AUs
observed in my sample. Some of these criteria are taken directly from the FACS manual (Ekman
et al., 2002a), while others are paraphrased from the original source to emphasize the most
visually-distinctive effect of each action in nonhuman anthropoids. Figure 3 provides examples
of AU 17 (Chin Raiser) in three species of nonhuman anthropoid.
Data reliability
It is important to assess the reliability of these data, because there is an element of
subjectivity to the coding procedure. Reliability can be assessed with regard to both inter- and
intra-observer error. FACS is a standardized methodology in which researchers become
independently certified by taking an official test after completing a period of self-directed
training. The training materials are available in the form of a CD-ROM that can be purchased
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through the official FACS website (www.face-and-emotion.com). Certified FACS coders, such
as myself, are assumed to agree on the criteria necessary to code the presence of each AU
defined by the system (Ekman, 1982). Thus, high inter-observer reliability is built into the FACS
training procedure, at least for human subjects. However, this does not necessarily imply high
intra-observer reliability, especially when the subjects are nonhuman primates.
Intra-observer error is usually assessed by re-coding some small percentage of the
original behavioral events and estimating a correlation. To be conservative, I re-coded all
previously-coded behavioral events two years later. I also re-coded events representing
borderline observations that were ultimately excluded from the dataset the first time. Thus, both
errors of inclusion and exclusion were addressed. I focused on the degree of intra-observer error
with regard to the facial mobility estimates for each species, not the individual behavioral events,
because the former are the relevant comparative data for this study.
With 16 possible AUs and 12 species, there are a total of 192 presence/absence
observations in the dataset (Table 4). After recoding, I changed only 2 of 192 observations,
resulting in an estimated intra-observer reliability of ~99% (agreement on 190 of 192
observations). I added AU 1+2 to the facial mobility estimate of Gorilla gorilla (a previouslyborderline observation) and dropped AU 4 from Hylobates concolor. Thus, the individual intraobserver reliability estimates for each of these species are ~ 94% (agreement on 15 of 16
observations). Issues of observer error will be addressed further in the discussion section.
Statistical analysis
I tested the hypothesis that body size is positively correlated with facial mobility at the
species level, by performing a phylogenetically-informed regression analysis. Data on female
body mass were taken from Smith and Jungers (1997) and used as estimates of species body size.
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Females were used as a minimum estimate of size, since several of the species in my dataset
exhibit significant sexual size dimorphism. Unassociated body mass values are routinely used in
comparative primatology. This is indicated by the citation count for Smith and Jungers (1997),
which is currently up to 232 (Web of Science). However, the use of unassociated body mass
values can increase the chances of Type II errors in correlation analysis.
Trait correlations were examined using multiple regression analysis with facial mobility
as the dependent variable. The independent effects of body mass and infraorder membership
were examined by entering both variables into the regression model as predictors. I included
infraorder membership because exploratory data analysis revealed a possible taxonomic effect.
This variable was dummy coded and treated as a continuous independent variable, which is
equivalent to an analysis of covariance (Cohen and Cohen, 1983). Body mass (g) was log
transformed (base 10) prior to analysis. I did not transform facial mobility because of its
relatively compressed range of variation (0 to 16).
It is now widely accepted that comparative studies must take into account phylogeny
(Blomberg and Garland, 2002; Nunn and Barton, 2001). The main justification for the use of
phylogenetic comparative methods is that interspecific data violate assumptions of independence
required of conventional statistics. With regard to regression, the main assumption that is often
violated is that the residuals are independent (Martins and Hansen, 1997; O’Neill and Dobson,
2008). Residual independence can be attained directly by modifying the error structure of the
regression using the phylogenetic generalized least-squares (PGLS) method (Martins and
Hansen, 1997). The error term in PGLS regression is represented by a phylogenetic-covariance
matrix, which is weighted by the parameter (Garland and Ives, 2000; Martins and Hansen,
1997; Rohlf, 2001). When = 0, phylogenetic signal is maximized. As increases, phylogenetic
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covariance among the residuals decreases. Regression coefficients and standard errors are
estimated using the phylogenetically-informed error structure. Lack of strong phylogenetic signal
in a dataset is not a problem for the PGLS method (Martins et al., 2002).
I performed PGLS multiple regression using COMPARE version 4.6b (Martins, 2004).
The phylogeny and associated branch lengths were taken from a larger composite primate
phylogeny (Smith and Cheverud, 2002). Maximum-likelihood was used to estimate within the
range 0 to 15.5, because estimates greater than 15.5 approximate the error structure of
conventional, non-phylogenetic regression (star phylogeny). COMPARE does not report P
values, but instead provides standard errors that can be used to construct confidence intervals for
hypothesis testing. If the residuals are independent, normally-distributed, and homoscedastic,
then a traditional 95% confidence interval is ± 1.96 standard errors. A statistically-significant
effect (P < 0.05) is represented by a 95% confidence interval that does not contain b = 0.
RESULTS
Table 4 presents facial mobility data for 12 nonhuman anthropoids, along with the
number of occurrences of each action unit (AU) in the comparative sample. Three AUs are
common to all species in the sample: AU 12 (Lip Corner Puller), AU 16 (Lower Lip Depressor),
and AU 18 (Lip Pucker). The other 13 AUs range in occurrence from zero for AU 20 (Lip
Stretcher), which is not present in any of the species in my sample, to 11 for AU 1+2 (Brow
Raiser), which is absent only in Saguinus oedipus.
There is considerable variation in facial mobility among the species in my sample (Table
4). Estimates range from 3 AUs in Saguinus oedipus to 13 AUs in Pan troglodytes and Gorilla
gorilla. There is also an observable taxonomic effect at the level of the infraorder (Fig. 4). On
average, catarrhines have the most mobile faces (Mean = 10.0; SD = 0.78; N = 8), while
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platyrrhines have lower facial mobility (Mean = 6.3; SD = 1.38; N = 4). However, two
platyrrhines, Ateles geoffroyi and Alouatta caraya, fall within the catarrhine range of variation.
Figure 4 presents a scatter plot depicting the relationships between facial mobility, body
mass, and infraorder membership. Larger species tend to have greater facial mobility, meaning
that they can produce a greater variety of facial movements. While catarrhines tend to exhibit
greater facial mobility than platyrrhines on average, there is no visual indication of a grade shift
between taxonomic groups, because the catarrhines in my sample also tend to be larger than the
platyrrhines. Multiple regression analysis supports these observations.
The results of a multiple regression analysis based on phylogenetic generalized least
squares (PGLS) are presented in Table 5. The maximum-likelihood estimate of = 15.5
indicates the presence of a strong evolutionary constraint on the relationship between facial
mobility and the two independent variables, body mass and infraorder membership. In other
words, there is minimal phylogenetic covariance among regression residuals, suggesting the
possible effect of stabilizing selection (Martins et al., 2002). The PGLS regression model
explains 92% of the variance in facial mobility. Tests of normality indicate that the PGLS
residuals satisfy the distributional assumptions necessary for using confidence intervals to test
the statistical null hypothesis b = 0 (Table 5).
The regression slope confidence interval for body mass does not contain zero, indicating
a statistically-significant partial effect (Table 5). That is, body mass predicts facial mobility
independent of infraorder membership. In contrast, the difference in facial mobility between
platyrrhines and catarrhines, controlling for body mass, is not statistically significant by this
same criterion. This suggests that taxonomic differences in facial mobility are driven by body
size. However, it should be noted that the infraorder sample sizes and the ratio of cases to
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variables are small. Despite these limitations, the overall effect of body size on facial mobility
appears robust (Fig. 4).
DISCUSSION
Evolutionary issues
Earlier studies of primate facial expression by Andrew (1963a,b, 1964) and van Hooff
(1962, 1967) were explicitly evolutionary and comparative. Since then, most research has
focused on the behavioral contexts and social functions of facial displays in particular species.
Schmidt and Cohn (2001) have called for an evolutionary approach to facial expression, as an
adjunct to the very important work of discerning social contexts. This renewed focus is evident
in recent comparative studies of the muscles of facial expression (Burrows and Smith, 2003;
Burrows et al. 2006; Burrows, 2008; Waller et al., 2008) and neural bases of facial motor control
in primates (Sherwood 2005; Sherwood et al., 2005). In addition, with the development of
ChimpFACS, it is now possible to rigorously assess hypotheses of evolutionary homology
between human and chimpanzee facial displays (Parr et al., 2007). Nonetheless, our
understanding of the evolution of facial expression as a system of communication is still in its
infancy. The question of allometric constraints is a necessary piece of the puzzle.
Body size is highly correlated with facial mobility in my sample of nonhuman
anthropoids, such that larger species tend to produce a greater variety of facial movements. This
allometric effect remains after controlling for phylogeny and is independent of infraorder
membership. Thus, taxonomic differences in facial mobility are driven largely by differences in
body size. This result lends support to the hypothesis that body size is a factor constraining the
adaptive evolution of facial expression (Moynihan, 1967). The implication is that small-bodied,
gregarious species are under selection pressure to emphasize other modes of communication,
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such as auditory or olfactory signals. Much more comparative work is necessary to determine the
validity of this claim.
While larger body size allows for greater facial mobility, it does not necessarily lead to an
increased reliance on facial expression (Moynihan, 1967). A comparison of Gorilla gorilla and
Pan troglodytes is telling in this regard. Both species can produce up to 13 AUs, which is the
largest facial mobility estimate for any nonhuman anthropoid in my sample. This value is only 3
AUs less than what a human can do. However, gorillas are not well known for their use of facial
displays. Instead, they rely more heavily on a rich repertoire of body movements, or gestures, for
social communication (Pika et al., 2003). In contrast, common chimpanzees regularly use a large
number of complex facial displays to mediate within-group social dynamics (van Hooff, 1973;
Parr et al., 2005). Similarly, Moynihan (1967) noted that although howler monkeys (Alouatta)
are among the largest platyrrhines, they do not exhibit as many facial displays as the other
members of Atelinae. Thus, large body size may provide the opportunity for selection with
regard to the complexity of facial expression, but not all large-bodied species are under the same
selection pressure to use complex facial displays.
The positive correlation between facial mobility and body size is likely related to
allometric constraints on the visual perception of facial movements (Moynihan, 1967). The
evolution of facial expression is contingent upon pre-existing adaptations of the visual system
(van Hooff, 1962, 1967). Facial expressions are processed at two levels, perception and
recognition (Adolphs, 2002). Perception encompasses low-level processing of the structure of an
image. Recognition refers to higher-level processing of social meaning and is associated with
species-specific neural pathways relating facial patterns to behavioral contexts. Perception, as
opposed to recognition, is dependent on visual acuity. Visual acuity is positively correlated with
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body size in birds and mammals, due to the fact that larger-bodied species have larger eyes
(Kiltie, 2000). Larger eye size results in the projection of a larger image onto the retina, which
means more photoreceptor cells are involved in processing the image and resolution is increased
(Walls, 1942; Ross, 2000; Kirk, 2004). Thus, the adaptive value of complex facial expression via
a wide variety of facial movements may be negligible at small sizes because small eye size limits
visual acuity. This interpretation rests on the following two assumptions: (1) the relationship
between visual acuity and body size is not strongly allometric, and (2) expertise in face
processing is a function of visual acuity.
I have interpreted the correlation between body size and facial mobility in light of visual
acuity. This assumes that body size is a proxy for visual acuity and that one variable can be
substituted for the other in allometric studies. Body size is highly correlated with visual acuity in
diurnal birds and mammals, with an interspecific correlation coefficient of r = 0.93 (Kiltie,
2000). However, this is not sufficient reason to substitute one measure for the other (Smith,
1981). The use of proxies in allometric studies requires that the relationship between the two
variables is isometric or at least not strongly allometric (Smith, 1981). For example, if the
relationship between body size and visual acuity is strongly negatively allometric, then body size
will overestimate visual acuity at larger sizes and underestimate acuity at smaller sizes. Kiltie
(2000) has demonstrated that the relationship between body size and visual acuity in diurnal
birds and mammals is roughly isometric. This is because visual acuity scales against eye size
with strong positively allometry, while eyes size is strongly negatively allometric in relation to
body size (Ross and Kirk, 2007). Thus, my assumption that the relationship between body size
and visual acuity is not strongly allometric appears to be valid.
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I have also assumed that expertise in processing facial expressions is constrained by
visual acuity, even though these signals are used in close proximity. This assumption is
supported by literature in human developmental psychology and neurobiology. While human
infants are capable of recognizing facial expressions to a limited extent (Nelson, 1987), children
do not start to reach adult levels of expertise in discriminating between expressions until 6-8
years (Mondloch et al, 2003). In addition, newborns have 40 times worse visual acuity than the
average adult (Maurer and Lewis, 2001). This is due in part to the immaturity of the eye. Infants
have smaller eyes and less densely-packed photoreceptors than adults, which limits visual acuity
(Daw, 2006). Humans do not reach adult levels of acuity until approximately 4-6 years (Maurer
and Lewis, 2001). This is immediately prior to the time that children start to discriminate
between facial expressions at adult levels of expertise. Thus, the development of facial
expression processing in humans seems to be constrained by the development of visual acuity,
despite the fact that facial expressions are typically used in close proximity.
The potential correlation between visual acuity and facial expression has implications for
the evolution of the anthropoid visual system. Diurnal anthropoids have unusually high levels of
visual acuity compared to other diurnal mammals (Kirk and Kay, 2004). Anthropoids exhibit a
number of derived features of the peripheral visual system that relate to visual acuity, including
relatively low cornea to eye size ratios (Kirk, 2004; Ross and Kirk, 2007) and high densities of
photoreceptor cells in an all-cone fovea with low levels of retinal summation (Kay and Kirk,
2000; Ross, 2000). The fact that diurnal anthropoids are surpassed only by some large diurnal
raptors with regard to visual acuity (Kirk and Kay, 2004) has led to the suggestion that
anthropoids originally evolved to fill a diurnal visual-predation niche with a focus on insectivory
(Ross, 2000). However, this lifestyle is uncommon among extant anthropoids. Furthermore, there
19
is considerable diversity in the visual system of extant anthropoids that remains to be explained
(Kirk and Kay, 2004). Therefore, I hypothesize that high visual acuity may have first evolved in
early anthropoids as an adaptation for diurnal visual predation (Ross, 2000), but that this trait has
been maintained and elaborated upon as an adaptation for processing visual signals, including
facial expressions. More comparative work is necessary to test the predictions of this hypothesis.
In addition to allometric constraints on perception, there are likely structural constraints
on the production of facial movements. Facial mobility is related to the anatomical
differentiation of the muscles of facial expression (Huber, 1931). While reliable comparative
data on facial muscle differentiation are rare (Burrows, 2008), the results of a recent comparative
analysis of the facial motor nucleus imply that larger species have more muscles of facial
expression than smaller species (Sherwood et al., 2005). The volume of the facial nucleus is an
estimate of the absolute number of motor neurons traveling from the brainstem to the muscles of
facial expression. Both an increase in muscle size and differentiation (e.g., more muscles) would
necessitate an increase in facial nucleus volume. There are considerable differences between
anthropoid superfamilies with regard to the absolute size of the facial nucleus (Sherwood et al.,
2005). These differences parallel taxonomic differences in body size, such that hominoids have
the largest facial nuclei on average, ceboids have the smallest nuclei, and cercopithecoids are
intermediate. The fact that larger anthropoids also produce a greater variety of facial movements
suggests that facial nucleus allometry is not only a consequence of increased muscle size in
larger species. Larger species may also have more muscles of facial expression, which may
contribute to their higher facial mobility.
Structural constraints on the production of facial movements may also help explain the
taxonomic differences between strepsirrhines and haplorrhines with regard to facial expression.
20
Most strepsirrhines produce a few relatively simple facial displays, with broad social functions
(Andrew, 1963a,b, 1964). In contrast, anthropoid facial displays tend to have multiple
components (van Hooff, 1962, 1967) and specific functions that are essential to group dynamics
(e.g., Flack and de Waal, 2007). This suborder difference may be due in part to the fact that
strepsirrhines have a moist rhinarium that attaches to the maxilla at the gums (Martin, 1990).
Having the upper lip tethered to the jaw may limit the variety of facial movements. Thus, the
evolution of strepsirrhine social communication may have been biased toward other sensory
modes, such as olfaction (Kirk, 2004).
Despite the strong relationship between facial mobility and body size among anthropoids,
it is likely that social factors have also influenced the evolution of facial mobility. Andrew
(1963a) hypothesized that increased facial mobility has evolved independently in each primate
superfamily, as the result of natural selection for conspicuous communication. Because facial
displays are composite signals consisting of multiple components (van Hooff, 1962, 1967),
individuals with more mobile faces have the potential to increase the number of elements in any
given display. In theory, adding elements to a composite signal can enhance its effectiveness in a
variety of ways, thereby increasing the probability of eliciting the desired response from the
receiver (Partan and Marler, 1999, 2005). Redican (1975, 1982) emphasized the functional
importance of adding elements to facial displays in terms of the intensity of the visual signal. For
example, a direct stare represents a low-intensity threat in many species (Emery, 2000). As other
facial movements, such as brow raising and lip tightening, are added to the stare, the threat
becomes more and more intense. Thus, greater facial mobility may permit greater potential
intensity. If so, interspecific variation in facial mobility may be correlated with variation in social
organization due to selection (e.g., Andrew, 1963a).
21
To date, no socioecological correlates of facial expression have ever been demonstrated
at the species level, and few formal hypotheses relating social organization to patterns of facial
expression exist (Maestripieri, 1999). The only study to directly examine the relationship
between any aspect of social organization and facial expression is the comparative study of the
facial nucleus by Sherwood et al. (2005). They did not find a correlation between social group
size and facial nucleus volume, controlling for medulla volume, in a sample of 47 primates. This
suggests that sociality is not a systematic influence of the evolution of facial motor control. Until
we know more about the evolutionary relationship between social organization and facial
expression, comparative studies of the adaptive evolution of this system of communication will
be hindered.
Methodological issues
I have presented a method for estimating facial mobility in nonhuman anthropoids, based
on an application of the human Facial Action Coding System, or FACS (Ekman et al., 2002a,b).
This system requires adjustments of emphasis for certain action units (AUs) to be applied to
nonhuman anthropoids (Vick et al., 2007). This is because differences between human and
nonhuman anthropoid facial structure can negate the usefulness of some FACS coding criteria.
My approach is based on a subjective assessment of the similarity between these coding criteria
and the appearance changes observed in nonhuman anthropoid faces. As with any observational
measurement technique, this approach should be validated using quantitative estimates of interobserver reliability before being applied more widely.
While I am unable to make definitive statements about inter-observer reliability, it is
possible to compare my results for Pan troglodytes with those obtained independently by the
ChimpFACS project (Vick et al. 2007). Table 6 provides a comparison between the present study
22
and Vick et al. (2007). My observations agree with ChimpFACS on 12 of 16 AUs (75%). The
discrepancies are due to my observation of four AUs that were not seen by Vick et al. (2007):
AU 13 (Sharp Lip Puller), AU 15 (Lip Corner Depressor), AU 18 (Lip Pucker), and AU 23 (Lip
Tightener). Most of my data come from facial actions coded during feeding, while Vick et al.
(2007) focus on communication. Thus, the differences between my study and Vick et al. (2007)
may be due to differences in sampling.
It is likely that there are also coding differences between my study and Vick et al. (2007).
First, according to FACS, an alternative description for AU 13 (Sharp Lip Puller) is “Cheek
Puffer,” which refers to the bulging of the infraorbital triangle in humans during this movement
(Ekman et al. 2002a). In Pan troglodytes, and other anthropoids that perform AU 13, it is the
sharp raising of the lip corners that is most evident based on my observations. Vick et al. (2007)
may have focused more on cheek puffing for their coding criteria. Second, with regard to AU 15
(Lip Corner Depressor), Vick et al. (2007) note that the relevant muscle is present and can be
electrically stimulated in Pan troglodytes (Burrows et al., 2006; Waller et al., 2006), but they
have not yet seen any clear examples of AU 15 acting independently. I was able to observe AU
15, but only in Pan troglodytes. Third, Vick et al. (2007) state that they have not observed any
action in chimpanzees that is comparable to the human AU 18 (Lip Pucker). I agree that AU 18
in nonhuman anthropoids is not comparable to the same action in humans in that the former do
not pucker their lips as if for a kiss (Vick et al., 2007). However, the species in my sample do
readily pull the lip corners medially, de-elongating the mouth, and they do so independent of AU
22 (Lip Funneler). Last, AU 23 (Lip Tightener) was not described by Vick et al. (2007) due to
the lack of permanently-everted lips in Pan troglodytes. The latter feature is not necessary to
code AU 23 if Lip Tightener is interpreted in the broad sense of tightening the skin around the
23
lower face. When nonhuman anthropoids perform AU 23, most of lower face tightens up,
causing wrinkling above and below the lips, as well as along the sides of the face. The
discrepancies between my study and ChimpFACS (Vick et al., 2007) may be reconciled through
future research.
In addition to the application of FACS to nonhuman anthropoids, a video sampling
procedure was also described, which can be used to estimate facial mobility at the species level.
The sampling procedure was deliberately designed to maximize both comparability between
species and efficiency of data collection. I observed a similar number of individuals in each
species (N = 2 to 5) using focal sampling, for the same amount of time (2 hrs), and at the same
time of day (morning feeding). I recorded animals only during feeding because preliminary
observations revealed that facial actions occur at a high rate in this context (> 1/min). Moreover,
it was not feasible to estimate facial mobility using only social behaviors (e.g., facial
expressions) in my study due to limitations on time and resources. While recording species
during feeding increases the rate at which facial actions can be observed, this approach may
diminish the relevance of the resulting facial mobility estimates with regard to the evolution of
facial expression. However, preliminary analyses of the relationship between facial mobility and
social group size, controlling for body size, suggest otherwise (Dobson, 2007).
The issue of frame rate must also be considered. I used a video camera with a 30 Hz
frame capture rate. This is the most common speed for analog and consumer-level digital video
cameras used in research (Polk et al., 2005). Because behavioral events that occur between
frames are subject to distortion and cannot be accurately identified, camcorders that record at
higher speeds are preferable, especially when collecting data on the duration and frequency of
behavioral events (Polk et al., 2005). However, frame rate is less of an issue for
24
presence/absence variables when sample size (e.g., the number of frames) is large. At 30 frames
per second, 2 hrs of video results in 216,000 frames per species. This appears to be sufficient to
estimate facial mobility at the species level (Fig. 2), due in part to the small number of possible
behaviors that can be observed. Moreover, collecting data on facial activity requires continuous
recording for long intervals of time. High-speed functions on conventional camcorders only
allow for recording short bursts of time (~ 3 seconds) due to the large amount of memory
consumed. The use of this function requires controlled observation conditions in which the
researcher can accurately predict when a behavior will occur next. Spontaneous facial behaviors
do not fit this criterion. Thus, while the use of a conventional 30 Hz frame rate is a possible
source of error in my study, the use of a high-speed function would have been impractical.
A related issue is the possibility that the correlation between body size and facial mobility
in my sample is due to a size-correlated observer bias. In particular, if smaller species contract
their muscles more quickly, then facial movements might be more difficult, if not impossible, for
a human to detect. Even if one ignores the fact that humans have the highest visual acuity of any
mammal (Kirk and Kay, 2004), this explanation is unlikely for the reason that shortening
velocities for muscles that are not involved in locomotion do not scale with body size (Medler,
2002). In other words, there is no evidence to suggest that smaller species contract their facial
muscles more quickly than larger species. For the sake of argument, we can assume that facial
muscles follow the same trend relating shortening velocity to body size observed for muscles
used in locomotion across a broad range of animal species. This relationship is described by the
allometric equation Vmax = 10.8 • mass-0.17, where Vmax is shortening velocity in lengths per
second and body mass is measured in grams (Medler, 2002). The regression coefficient in this
equation is negative, but very shallow at -0.17. Thus, even if the shortening velocities of facial
25
muscles do scale with body size, the effect would be minor and unlikely to account for major
differences between taxa with regard to observed facial mobility.
CONCLUSION
The evolution of facial expression is a topic of renewed interest in biological
anthropology (Schmidt and Cohn, 2001). An evolutionary perspective requires viewing facial
expression as a system of communication subject to rules and constraints. The fundamental goal
of a research program oriented toward evolutionary biology is to explain trait diversity. Not all
primates rely on facial expression, and some anthropoids rely more on this form of
communication than others. Allometric constraints are likely to be an important factor
influencing the evolution of facial expression. In particular, as my study demonstrates, smaller
species produce a smaller variety of facial movements and are therefore at a disadvantage from
the standpoint of possessing the raw materials necessary for complex facial communication.
While there is likely to be considerable adaptive diversity in facial expression among
anthropoids, researchers should always bear in mind the potential allometric constraints on this
important system of communication.
ACKNOWLEDGEMENTS
I thank the curators and keepers of primates at the St. Louis Zoo and Lincoln Park Zoo,
especially Susan Margulis and her staff (LPZ). Thanks to my dissertation committee, Richard
Smith, Jane Phillips-Conroy, Tab Rasmussen, Glenn Conroy, Charles Hildebolt, and Allan
Larson, for their guidance and criticism. Thanks to Robert Sussman for suggesting that I use
FACS. Thanks to Kathleen Muldoon, Christopher Ruff, and three anonymous reviewers for
helpful comments on earlier drafts of this paper. Thanks also to Chris Kirk and Matt O’Neill for
26
helpful discussions. Funding for this study was provided by the National Science Foundation
(Grant #0424160) and Sigma Xi Grant-in-Aid of Research.
27
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34
TABLE 1. Details of comparative sample
Species
Infraorder
Zoo a
N
Mass (g) b
Alouatta caraya
Platyrrhini
LPZ
4 (1, 3)
4,300
Ateles geoffroyi
Platyrrhini
STL
3 (0, 3)
7,290
Cercopithecus neglectus
Catarrhini
LPZ
2 (1, 1)
4,130
Colobus guereza
Catarrhini
LPZ
2 (1, 1)
9,200
Gorilla gorilla
Catarrhini
LPZ
4 (1, 3)
71,500
Hylobates concolor
Catarrhini
LPZ
2 (1, 1)
7,620
Macaca silenus
Catarrhini
STL
5 (3, 2)
6,100
Pan troglodytes
Catarrhini
LPZ
5 (1, 4)
33,700
Papio hamadryas
Catarrhini
STL
5 (1, 4)
9,900
Pithecia pithecia
Platyrrhini
STL
5 (3, 2)
1,580
Saguinus oedipus
Platyrrhini
STL
2 (1, 1)
404
Trachypithecus obscura
Catarrhini
STL
2 (2, 0)
6,360
a
Location of captive groups. LPZ = Lincoln Park Zoo. STL = St. Louis Zoo.
b
Female body mass from Smith and Jungers (1997)
35
TABLE 2. Action units (AUs) used to estimate facial mobility
AU
Description a
Muscular basis b
1+2 Brow Raiser
Frontalis (medial and lateral parts)
4
Brow Lowerer
Procerus, depressor supercilii, or corrugator supercilii
9
Nose Wrinkler
Levator labii superioris alaeque nasi
10
Upper Lip Raiser
Levator labii superioris
12
Lip Corner Puller
Zygomaticus major
13
Sharp Lip Puller
Caninus (levator anguli oris)
14
Dimpler
Buccinnator
15
Lip Corner Depressor
Triangularis (depressor anguli oris)
16
Lower Lip Depressor
Depressor labii inferioris
17
Chin Raiser
Mentalis
18
Lip Pucker
Incisivii labii and/or orbicularis oris
20
Lip Stretcher
Risorius
22
Lip Funneler
Orbicularis oris
23
Lip Tightener
Orbicularis oris
24
Lip Presser
Orbicularis oris
28
Lips Suck
Orbicularis oris
a
See Ekman et al. (2002a) for detailed descriptions of each AU in humans.
b
Muscle associations for Homo sapiens and Pan troglodytes only (Ekman and Friesen, 1976;
Waller et al., 2006).
36
TABLE 3. Most-useful FACS coding criteria for nonhuman anthropoids a
Action unit
1+2
Appearance change
Pulls the medial and lateral parts of the brow upwards
4
Lowers the entire brow region by pulling the anterior part of the scalp downward
9
Pulls the skin above the nose upward toward the orbits causing horizontal wrinkles
across the infraorbital region
a
10
Raises the upper lip causing the lips to part
12
Pulls the corners of the lips backward
13
Pulls the corners of the lips upward sharply without pulling them backward
14
Tightens the corners of the lips causing an oblique wrinkle at corner
15
Pulls the corners of the lips downward
16
Pulls the lower lip down causing the lips to part
17
Protrudes the lips
18
Pulls the lip corners medially causing the mouth opening to shrink
22
Parts and everts the lips causing them to turn outward
23
Tightens the lips causing vertical wrinkles above and below the lips
24
Presses the lips together causing bulging above and below the lips
28
Pulls the lips inward causing the skin to stretch over the teeth
Based on a subjective assessment of the similarity of nonhuman anthropoid muscle actions to
the criteria defined by the human Facial Action Coding System, or FACS (Ekman et al. 2002a).
See text for details.
37
TABLE 4. Facial mobility data for 12 nonhuman anthropoid species
Action units b
Species a
1+2 4
9
10 12 13 14 15 16 17 18 20 22 23 24 28
FM c
Alouatta
1
1
0
1
1
0
0
0
1
1
1
0
0
0
0
1
8
Ateles
1
1
0
1
1
0
0
0
1
1
1
0
1
0
0
1
9
Cercopithecus
1
0
0
1
1
1
0
0
1
0
1
0
0
0
0
1
7
Colobus
1
0
1
1
1
1
0
0
1
1
1
0
0
1
0
0
9
Gorilla
1
0
1
1
1
1
1
0
1
1
1
0
1
1
1
1
13
Hylobates
1
0
1
1
1
0
0
0
1
1
1
0
0
1
0
0
8
Macaca
1
0
1
1
1
1
0
0
1
1
1
0
0
1
0
1
10
Pan
1
0
1
1
1
1
0
1
1
1
1
0
1
1
1
1
13
Papio
1
0
1
1
1
1
1
0
1
1
1
0
0
1
0
1
11
Pithecia
1
1
0
0
1
0
0
0
1
0
1
0
0
0
0
0
5
Saguinus
0
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
3
Trachypithecus 1
0
1
1
1
0
0
0
1
1
1
0
0
1
0
1
9
Total d
3
7
10 12
6
2
1
12
9
12
0
3
7
2
8
11
a
See Table 1 for full species names.
b
1 = present, 0 = absent. See Tables 2 and 3 for descriptions of each action unit.
c
FM = Facial mobility estimate. See text for details.
d
Total number of species exhibiting a given action unit.
38
TABLE 5. Results of multiple regression based on phylogenetic generalized least squares a
r
Variable
Slope (b)
Standard error
Confidence interval b
0.96
Body mass
2.08
0.27
1.55, 2.61
Infraorder
0.26
0.73
-1.17, 1.69
a
Dependent variable: facial mobility (AUs). See text for details.
b
95% confidence limits for b (lower, upper). Calculated as b ± 1.96*standard error. Assumptions
of normality for regression residuals cannot be rejected based on one-sample KolmogorovSmirnov test (P > 0.05). Intervals that do not overlap zero indicate a statistically-significant
partial effect (i.e., rejection of b = 0).
39
TABLE 6. Comparison of facial action units observed for Pan troglodytes in two studies a
Action unit b
This study
Vick et al. (2007)
1+2
Yes
Yes
4
No
No
9
Yes
Yes
10
Yes
Yes
12
Yes
Yes
13
Yes
No
14
No
No
15
Yes
No
16
Yes
Yes
17
Yes
Yes
18
Yes
No
20
No
No
22
Yes
Yes
23
Yes
No
24
Yes
Yes
28
Yes
Yes
a
Overall agreement between studies = 75% (12/16). See text for further details.
b
See Tables 2 and 3 for details regarding action units.
40
FIGURE LEGENDS
Fig. 1. Action units (AUs) used to define the human facial expression of fear (Ekman and
Friesen, 2003) based on the Facial Action Coding System (Ekman et al., 2002a). AU 1 = Inner
Brow Raiser. AU 2 = Outer Brow Raiser. AU 4 = Brow Lowerer. AU 5 = Upper Lid Raiser (not
labeled). AU 20 = Lip Stretcher. The AUs are labeled unilaterally, but the actions are present on
both sides of the face. The circles are used to illustrate approximate muscle origin points. Each
muscle pulls in the direction of the arrow (toward the circle) when contracted. See text further
for details. Photo by K. Muldoon.
Fig. 2. Line graph of the number of facial events coded per 10-minute sample in each species.
The observation accumulation curves indicate that 2 hrs (12 samples) is more than enough time
to estimate facial mobility at the species level. Each curve declines to zero after 9 samples (90
minutes) or less. This drop off occurs because once an action unit (AU) was observed four times,
it was no longer coded in subsequent samples. Thus, the curves indicate that the number of
“new” observations declines rapidly after the first few sampling bouts, then plateaus prior to the
end of the total sampling period.
Fig. 3. Still images of action unit 17 (Chin Raiser) captured from video recordings of three
nonhuman anthropoids: (a) Pan troglodytes, (b) Alouatta caraya, and (c) Papio hamadryas. The
Facial Action Coding System (Ekman et al., 2002a) does not emphasize lip protrusion in the
coding criteria for this movement, because the main effect in humans is the upward movement of
the chin boss and lower lip. This is not the case in most nonhuman anthropoids. Action unit 17 is
41
mainly a forward rather than upward action in nonhuman anthropoids. It protrudes the lower lip
in particular. This movement may or may not correspond to the action of the mentalis muscle
(Table 1).
Fig. 4. Scatter plot illustrating the relationships between facial mobility, body mass, and
infraorder membership in 12 species of nonhuman anthropoids (data from Table 4). The closed
circles indicate platyrrhines, while the open circles are for catarrhines. The species are: Alouatta
(1), Ateles (2) , Cercopithecus (3), Colubus (4), Gorilla (5), Hylobates (6), Macaca (7), Pan (8),
Papio (9), Pithecia (10), Saguinus (11), and Trachypithecus (12). See Table 2 for complete
species names. The line represents an ordinary least-squares fit to the data. See Table 5 for
multiple regression results. There is a strong positive correlation between facial mobility and
body mass independent of infraorder. However, infraorder is not a good predictor of facial
mobility controlling for body mass.
42
Fig. 1
43
Fig. 2
44
Fig. 3
45
46