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Personality traits in captive lion-tailed macaques (Macaca silenus).

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American Journal of Primatology 67:177–198 (2005)
Personality Traits in Captive Lion-Tailed Macaques
(Macaca silenus)
Department of Anthropology, Washington University in St. Louis, St. Louis, Missouri
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
Personality influences an individual’s perception of a situation and
orchestrates behavioral responses. It is an important factor in elucidating
variation in behavior both within and between species. The major focus of
this research was to test a method that differs from those used in most
previous personality studies, while investigating the personality traits of
52 captive lion-tailed macaques from four zoos. In this study, data from
behavioral observations, a P-type principal components analysis (PCA),
and bootstrapped confidence intervals as criteria for judging the
significance of factor loadings were used rather than subjective ratings,
R-type factor analyses, and arbitrary rules of thumb to determine
significance. We investigated the relationships among individual component scores and sex, hormonal status, and dominance rank (controlling
for age and social group) using a multiple regression analysis with
bootstrapped confidence intervals. Three personality dimensions
emerged from this analysis: Component 1 contained Extraversion-like
behaviors related to sociability and affiliativeness. The higher mean
Component score for females suggests that they are more ‘‘extraverted’’
than males. Only agonistic behaviors were significantly related to
component 2. High-ranking individuals exhibited higher mean Component 2 scores than mid- or low-ranked individuals. Bold and cautious
behaviors both loaded positively on Component 3, suggesting a dimension
related to curiosity. The mean Component 3 score for females was higher
than the mean score for males. The method used in this study should
facilitate intraspecific and general interspecific comparisons. Developing
a standardized trait term list that is applicable to many species, and
collecting trait term data in the same manner and concurrent with
behavioral observations (and physiologic measures when feasible) could
prove useful in primate research and should be explored. Am. J. Primatol.
67:177–198, 2005.
r 2005 Wiley-Liss, Inc.
Contract grant sponsor: National Science Foundation; Contract grant number: 9816165; Contract
grant sponsor: Sigma Xi; Contract grant sponsor: Scientific Research Society; Contract grant
sponsor: Washington University in St. Louis.
Correspondence to: Jacqueline Rouff, 1633 Shepard Road, Glencoe, MO 63038.
Received 22 April 2004; revised 15 February 2005; revision accepted 16 February 2005
DOI 10.1002/ajp.20176
Published online in Wiley InterScience (
2005 Wiley-Liss, Inc.
178 / Rouff et al.
Key words: personality; personality traits; lion-tailed macaques;
Macaca silenus
Personality–identifiable styles of behavior that differentiate one individual
from another–reflects the dynamic organization within the individual of the
psychobiological systems that modulate adaptation to a changing environment
[Allport, 1937; Svrakic et al., 1996]. Researchers who use the ‘‘trait approach’’
view human personality as consisting of a limited number of bipolar dimensions.
These are the stable, superordinate factors that influence an individual’s
perception of a situation and orchestrate the behavioral responses [Capitanio,
1999; Capitanio et al., 1999]. The trait approach to personality is particularly
appropriate for nonhumans because one can assess personality traits by
measuring observable behaviors, such as sociability and aggression. In addition,
many phenotypic personality traits appear to have a genetic component and/or
are associated with individual differences in emotional experience in humans,
which has led to a growing recognition that major personality traits represent
basic psychobiological dimensions of temperament [Clark & Watson, 1999;
Gillespie et al., 2003; Watson, 2000].
Understanding individual differences in personality traits would increase our
knowledge of variation both within and between primate species. Primate
researchers studying diverse topics have suggested that personality traits are
important factors in explaining the results of their studies. For example,
personality has been used to explain why certain individuals gain dominance
[Burton, 1992; Huff, 2002; McGuire et al., 1994; Pavelka, 1997; Rhine &
Maryanski, 1996; Saltzman et al., 1996; Sapolsky, 1990; Virgin & Sapolsky, 1997;
Zumpe & Michael, 1996], differences in maternal behavior [Bahr, 1995; Bard,
1995; Brock, 1998; Fairbanks, 1996], grooming preferences and activity [Borries
et al., 1994], and an animal’s approach to learning in studies of cognition [Boysen,
1994]. The personality of available males may influence female mate choice
[Goodall, 1986; Keddy-Hector, 1992; Price, 1990; Small, 1989; Smuts, 1985, 1987].
Certain personality traits predict cortisol levels in male baboons and differences
in response to stress and immune response in baboons and macaques [Capitanio
& Mendoza, 1995; Capitanio et al., 1994; Kemeny & Laudenslager, 1999; Lilly,
1995; Sapolsky, 1990, 1991, 1999; Sapolsky & Ray, 1989; Suomi, 1991].
In addition, understanding individual differences in personality can enhance
our ability to study other determinants of primate behavior. The omission of
individual differences in personality relegates it to the error term, which can
make it harder to detect the effect of other key independent variables [Chamove
et al., 1972]. For example, failure to account for personality differences may cloud
the results of stress studies, because the effects of a particular stressor may vary
with the characteristics of the individuals involved.
The results of numerous studies indicate a link between several traits and
the monoaminergic neurotransmitter systems. Serotonin levels are known to
correlate positively with affiliation, and negatively with overly aggressive and
impulsive behaviors (see Higley and Bennett [1999] and Higley et al. [1994] for
summaries of this research). The noradrenergic system appears to be related to
differences in reactivity to novel or challenging circumstances [Suomi, 1991].
Species differences have been noted in behavioral and/or physiological responses
Lion-Tailed Macaque Personality Traits / 179
to novel or challenging stimuli [Bernstein et al., 1963; Clarke et al., 1994, 1995;
Singh & Manocha, 1966; Vitale et al., 1991], as well as in levels of CSF 5-HIAA
and affiliative and aggressive behaviors [Champoux et al., 1997; Higley &
Bennett, 1999]. A better understanding of the biological basis and heritability of
personality traits and formative environmental factors could help us understand
how individuals are constrained by evolved predispositions, and how these are
related to social systems [Boinski, 1999; Clarke & Boinski, 1995; Steklis, 1993].
For example, do personality traits differ between female-bonded and non-femalebonded societies? Are differences between female and male dispersal patterns
related to personality traits?
Developing reliable methods to facilitate within- and cross-species comparisons is essential for nonhuman primate personality research, because the
potential contribution to primatology of such research remains largely untapped.
Methodological Differences From Past Studies
The purpose of this research was to test a method that differs from those
used in most previous personality studies, while investigating the personality
traits of the lion-tailed macaque (Macaca silenus) and the relationships of sex,
hormone status, and dominance rank to these traits. In this study, observed
behaviors were used as data, which were analyzed with a P-type principal
components analysis (PCA). Bootstrapped confidence intervals were then constructed to evaluate which behaviors were significantly associated with a component. This method differs from those used in most previous studies, which used
rules of thumb to determine the significance of factor loadings, as well as R-type
factor analytic methods, and which relied primarily on subjective assessments.
With subjective assessments, each monkey or ape is rated on adjective trait
terms (such as sociable, excitable, and popular), most often only once or one time
per year, which makes these ratings vulnerable to memory biases. Both subjective
rating and behavioral coding methods have their advantages and disadvantages,
and behavior-based studies are not bias-free. Coding methods can introduce bias
through the behaviors chosen to represent traits and the methods of observation
and coding selected. However, relative to rating data, behavioral data have a more
absolute metric and are less vulnerable to framing effects, and can provide
meaningful comparisons among individuals, groups, and species–provided that
equivalent species-specific behaviors are compared.
The behavioral data in this study were analyzed with a P-type PCA rather
than the R-type used in past studies. The P-type approach provides a perspective
on personality that is a valuable alternative to that produced by the R-type
analysis. In an R-type analysis, the data for each trait or behavioral category are
averaged across the study for each subject. A single correlation matrix is
calculated that represents covariation of behaviors across the sample of animals,
and this matrix is then subjected to a PCA. In a P-type analysis, the data for each
individual are not averaged across sample periods. Rather, a correlation matrix
for each individual is calculated using the sample periods for each monkey in
place of the aggregated data of each subject (as in the R-type analysis). In this
study, the correlation matrix for each individual indexed the covariation of
behaviors across the sample periods for that individual. These individual matrices
were then averaged to form the correlation matrix for the PCA. In this sense, the
P-type approach used in this study is best thought of as a combination of
idiographic and nomothetic approaches, in that the goal of averaging the
180 / Rouff et al.
individual animal matrices is to allow inferences to be made about the average
The key difference between the two approaches results from the nature of the
correlations that enter the PCA, and consequently the nature of the components
that emerge. In the P-type analysis, the correlations in the PCA reflect the typical
covariation of behaviors across samples. In the R-type analysis, the correlations
represent the typical covariation of behaviors across animals. A positive
correlation in the R-type approach means that when a particular animal exhibits
a high amount of X (averaged over situations), that same animal also tends to
exhibit a high amount of Y (averaged over situations). However, that correlation
does not provide any information about the covariation of these traits or
behaviors within each individual across situations. By contrast, a positive
correlation in the P-type approach means that when a particular behavior occurs
frequently across situations, another behavior tends to occur frequently across
situations as well, for the average animal. The difference in the two approaches
explains why traits from nomothetic analyses yield information on trends or
tendencies to behave in certain ways, but are poor at predicting or describing
momentary behaviors [Fleeson, 2004; Mischel, 1999]. The relationship between
different traits within an individual yields important information about
personality that cannot be obtained through nomothetic comparisons alone
[Allport, 1937]. An R-type analysis is certainly an appropriate choice for
investigating the structure of personality. However, important information about
personality can be lost that might otherwise be captured if idiographic and
nomothetic techniques are integrated, as they are in the P-type analysis used
in this study [Allport, 1937; Fleeson, 2001, 2004; Mischel, 1999; Pelham, 1993].
The P-type analysis is a good choice when the goal is to describe personality with
the patterns of behaviors over time for individuals.
One problem with factor-analysis techniques in primate research is that it is
often difficult to obtain the large sample sizes required to obtain stable results.
Researchers in many earlier studies used too few subjects for the number of
variables, which can produce factors that fail to replicate, and large factor
loadings by chance alone. To decrease the possibility that we would interpret
chance factor loadings as meaningful in this study, we constructed confidence
intervals for the factor loadings using bootstrapping techniques rather than
relying on rules of thumb.
The traits that were included in this study were drawn from previous
nonhuman primate personality research, as well as from aspects of two popular
human personality measures: the ‘‘Big Five Factor Model’’ and Cloninger’s
Temperament and Character Inventory (TCI) [Cloninger et al., 1994]. We
selected two temperament factors (Persistence and Novelty Seeking) from the
TCI, and three factors (Extraversion/Introversion, Neuroticism/Emotional Stability, and Agreeableness) from the Big Five Factor Model. The traits associated
with these factors appeared similar to traits that were of interest to researchers in
previous nonhuman primate studies, and could also be represented by behaviors
commonly observed in captive macaques.
Many studies of nonhuman primates and other species (e.g., rats [GarciaSevilla, 1984], guppies [Budeav, 1997], cats [Gosling, 1999; Saxton et al., 1987],
octopuses [Mather & Anderson, 1993], hyenas and dogs [Gosling, 1999], and
nonhuman primates [Bard & Gardner, 1996; Bolig et al., 1992; Capitanio, 1999;
Lion-Tailed Macaque Personality Traits / 181
Chamove et al., 1972; Dutton et al., 1997; Gold & Maple, 1994; King & Figueredo,
1997; Laudenslager et al., 1999; McGuire et al., 1994; Mondragon-Ceballos et al.,
1991; Mondragon-Ceballos & Santillan-Doherty, 1994; Murray, 1996; StevensonHinde et al., 1980a; van Hooff, 1970]) have found some evidence of factors that
appear similar to some or all of the three Big Five Factors selected for this study.
In humans, these three factors have shown cross-cultural stability and heritability
[McCrae & Costa, 1997; Rowe, 1994; Steen, 1996]. There is also evidence of a
biological basis and heritability in humans for Novelty Seeking and Persistence,
the two factors selected from the TCI [Gusnard et al., 2003; Youn et al., 2002].
Trait Adjectives and Ethogram
The human personality models and nonhuman primate personality factors
found in previous studies aided us in selecting traits for this study that were
important and of interest to personality researchers. Five Extraversion facets
(gregariousness, activity, warmth, seeks excitement, and assertiveness), two
Neuroticism facets (anxiety and vulnerability), and four adjective pairs for the
domain of Agreeableness (aggressive/protective, agonistic/gentle, suspicious/
trusting, and insensitive/sensitive) were selected. Facets such as positive
emotions (cheerful vs. serious) from Extraversion, and depression (hopeless vs.
hopeful) from the Neuroticism factor were omitted because these required us to
infer an emotional state that seemed difficult to assess from observed behaviors.
Facets such as impulsiveness were not included, because a behavioral measure
could not be found that was applicable to all captive groups and was feasible in
a zoo setting (where researchers must accommodate zoo policies and keeper
routines). The adjectives ‘‘welcome’’ (others allow focal animal to approach) and
‘‘avoided’’ (others move away when the focal animal approaches) were added as a
measure of whether other group members found the focal animal agreeable or
disagreeable. We included aspects of Cloninger’s [1994] TCI factors by adding
‘‘persistence’’ and defining bold and cautious behaviors as ‘‘response to novelty.’’
We adapted definitions of selected traits for use with nonhuman primates
using Stevenson-Hinde et al.’s [1980b] adjectives and definitions whenever
possible. Specific behaviors to represent trait adjectives were selected from an
ethogram of the lion-tailed macaque in captivity [Johnson, 1985; Skinner & Lockard,
1979], from Stevenson-Hinde et al.’s [1980b] behavioral definitions of traits for
rhesus macaques (when available and appropriate), or based on behavioral observations of two captive groups of lion-tailed macaques at the St. Louis Zoo. The
names of several trait adjectives were changed to terms that are frequently used
in other primate studies, or to link trait terms more closely to their representative
ethogram behaviors. The following substitutions were made: ‘‘proximity’’ for
‘‘gregariousness,’’ ‘‘affiliativeness’’ for ‘‘warmth,’’ ‘‘response to novelty’’ for ‘‘seeks
excitement,’’ ‘‘dominance’’ for ‘‘assertiveness,’’ ‘‘reactivity’’ for ‘‘vulnerability,’’
‘‘intervenes’’ for ‘‘protective,’’ ‘‘moves away’’ for ‘‘suspicious,’’ ‘‘approachable’’
(i.e., permits approach) for ‘‘trusting,’’ and ‘‘willing to interact’’ for ‘‘gentle.’’
To check the accuracy and reliability of the ethogram, three naive observers
independently coded videotaped lion-tailed macaque behaviors. The ethogram
definitions were revised until the observers could agree on the coding and agree
that the behaviors represented the trait adjective. The revised ethogram was
further refined and tested for reliability while two observers coded behaviors of a
group of lion-tailed macaques at the St. Louis Zoo. The reliability of the ethogram
was tested again in a group of lion-tailed macaques at the Mesker Park Zoo, by
one naive observer and one individual who had participated in the prior test.
182 / Rouff et al.
Cohen’s Kappa for behavioral categories ranged from 0.88 to 1.00. Values of Kappa
greater than 0.75 indicate excellent agreement beyond chance [SPSS, 1997].
Table I contains more details on the traits included in this study, how they
were defined, and the types of behaviors recorded.
TABLE I. Personality Facets and Traits Included in This Study
Extraversion-like facets and behaviors
I. Proximity
Sociable – within 1 body length of
Solitary – more than 1 body length
from others
II. Activity level
Active – moves faster than 1 body
Slow – moves at or slower than 1 body
III. Affiliativeness
Affiliative – allogrooming, play,
Aloof – autogrooms, refuses to play, refuses to
non-aggressive physical contact
allogroom, stops interacting
IV. Response to novelty
Bold – interested in and closer than
Cautious – interested in but farther than 3
3 body lengths to a novel stimuli
body lengths from a novel stimuli
V. Dominance
Assertive – displaces another without Submissive – yields place, food, or affiliative
threat or aggression
relationship to another
Neuroticism-like facets and behaviors
I. Anxiety
Anxious – stiff, restrained posture
Relaxed – relaxed posture and movement
and movement; surveys environment;
when 2 others display anxious behaviors
relocates in response to interactions
of others when not approached; stereotypical behavior; scratching; yawning
(in non-sleepy context)
II. Reactivity
Reactive – reacts strongly to
Unreactive – does not react to nonthreatenenvironmental change
ing stimuli when 2 others are reactive
Agreeableness-like facets and behaviors
I. Intervenes (protective) – aids victim
Aggressive –attacks or chases another
of aggression or juvenile when play
becomes too rough
II. Interacts (gentle) – no threat or
Agonistic – threatens another
aggression when others initiate an
III. Approachable – does not move away Moves away (suspicious) – moves away
when approached by others
when approached
IV. Sensitive (to distress of others) –
Insensitive (to distress of others) – does
notices (looks at) an agonistic or
not notice (does not look at) an aggressive
aggressive interaction; touches
or agonistic interaction; steals or tries to
embraces, or grooms a fearful
steal an infant; pulls out hair of another
V. Welcome – others allow the focal
Avoided – others move away when focal
animal to approach
animal approaches
Persistent – attempts to gain an item or outcome after failing to obtain it (working for a
food item; re-approaching an individual following an agonistic or aggressive rebuff; or subsequent attempts to initiate play, grooming, or copulation after previous rejection)
Lion-Tailed Macaque Personality Traits / 183
The subjects were 52 lion-tailed macaques in eight different groups at four
different zoos (three groups at the Baltimore Zoo, two at the Center for
Reproduction of Endangered Species (CRES) of the San Diego Zoological Society,
one at the Mesker Park Zoo (Evansville, Indiana), and two at the Saint Louis Zoo).
The groups included in this study were selected to provide a sample that reflected
the different demographic and environmental conditions of lion-tailed macaques
in captivity.
Focal animal data.
Each group was observed on three separate occasions at least 90 days apart.
On each of these three occasions, each animal was observed for a total of 4 hr (12
hr per animal over the entire study, and 616 hr total for all animals). One
researcher collected the majority of the data included in the analysis (596 hr), and
a second individual collected the remaining 20 hr of data. The monkeys were
observed for a 30-min period using instantaneous sampling for a focal animal,
with ethogram behaviors coded as present or absent at 1-min intervals, yielding
30 sample points [Lehner, 1996]. The onset of all ethogram event behaviors was
also recorded. Only one 30-min sample was collected on any individual per day (24
samples total per individual). All macaque groups could see the observers and
were habituated to their presence. The Mesker Park, Baltimore Zoo Veterinary
Center, and CRES groups could easily hear the observers as well.
Before the data were collected, two observers checked the reliability of the
data collection by simultaneously coding the behavior of each individual in each
group for 30 min. A Cohen’s Kappa for interrater reliability was calculated for
each behavioral facet (e.g., sociable behaviors/solitary behaviors or reactive
behaviors/unreactive behaviors) for each group. Table II shows the mean Cohen’s
Kappa for all groups on each behavioral facet.
Response to novelty data.
A novel-objects study was conducted at the conclusion of the focal animal
study. Twelve novel objects (a plastic owl, dinosaur head puppet, small
magnifying mirror, large toy stuffed bird, plastic pumpkin, laptop computer with
the screen-saver program ‘‘Catz’’ running, large mirror, rubber snake, poster of
the head of a large felid, plastic frog that croaked when approached, pinwheel
blown by a battery-operated fan, and plastic human skull) were placed outside the
enclosure for 15 min each. Data were collected through scan sampling at 1-min
intervals, and it was noted whether each individual was bold (within three body
lengths and looking at the object), cautious (farther than three body lengths away,
and looking at the object), or uninterested in it. Before the study began, two
observers simultaneously recorded the bold, cautious, and uninterested behaviors
of each group in response to three different novel objects to check the reliability
of the data collection. Cohen’s Kappa ranged from 0.82 to 0.92.
Persistence data.
The collection of persistence data was complicated by the different
circumstances of each group and the different zoo policies, which created
different opportunities for ethogram persistent behaviors. Therefore, PVC tube
feeders, which required manipulation for an occasional food reward, were
184 / Rouff et al.
TABLE II. Mean Value of Cohen’s Kappa Calculated For Reliability of Facets Averaged
Across Groups
Activity level
Response to
Moves away/
Behaviors observed in only one group during sample periods (K=0.90). 100% agreement that behaviors did not
occur in other groups.
Behaviors observed in only five groups during sample periods. 100% agreement that behaviors did not occur in
other groups.
Behaviors observed in only six groups during sample periods. 100% agreement that behaviors did not occur in
other groups.
Behaviors observed in only three groups during sample periods. 100% agreement that behaviors did not occur
in other groups.
Behaviors observed in only four groups during sample periods. 100% agreement that behaviors did not occur in
other groups.
periodically placed in the enclosures. However, this was not feasible for the CRES
groups. In those groups, persistence was defined as digging through the grass/
weeds for seeds, bugs, etc. prior to the morning produce feeding, or prior to any
isolation of females for a hormonal study by another research group.
Data Analysis
The focal animal data were analyzed with a P-type PCA, and bootstrapped
confidence intervals were calculated for the resulting principal components
loadings. Twenty-six variables were included in the analysis: welcomed, avoided,
sociable, solitary, aloof (breaks off interaction + refuses to play/groom), affiliative,
self-grooming, active, slow, assertive, submissive, bold, cautious, anxiety (anxious
+ submissive gestures + salute + scratch + stereotypical behaviors + gape
yawn), relaxed, reactive, unreactive, aggressive, intervenes, moves away,
approachable, interacts, agonistic, insensitive, sensitive, and persistent.
In the P-type PCA, a correlation matrix was first calculated for each monkey.
Then the correlation matrices for each of the 52 subjects in the study were
aggregated by simple averaging. With simple averaging, any bias will be slightly
negative. This makes it a more conservative approach than Fisher z0
transformation, which can produce a positive bias [Strube, 1988]. By aggregating
the correlations from all individuals, we also dealt with the problem of increased
standard errors for correlations in the individual matrices that were based on a
small number of observations, as well as the effects of outliers from a single
correlation for any individual.
A PCA with Varimax rotation was performed on the averaged correlation
matrix for all monkeys in the study (n=52), with the use of Systat statistical
software. Three components were selected for further analysis based on the Scree
test and Horn’s [1965] random Scree procedure. In addition, PCAs were
conducted on the averaged matrices of subgroups of subjects to ensure that the
Lion-Tailed Macaque Personality Traits / 185
components used to develop component scores for each individual (which were
taken from the analysis of all animals) were representative of the entire sample.
The PCAs of these subgroups (all males (n=20), all females (n=32), intact and
vasectomized males (n=11), chemically or surgically castrated males or juvenile
males (n=9), cycling females (n=19), and noncycling females (postmenopausal,
spayed, contracepted, and juvenile females (n=13)) suggested that three
components provided a reasonably stable solution.
We then calculated regression-based component scores for each individual on
each of the three components by multiplying the standardized scores of
behavioral categories for each individual by a weight matrix derived from the
PCA of the averaged matrices for all of the monkeys in the sample.
Bootstrapping techniques [e.g., Hamilton, 1992; Hesterberg et al., 2003;
Mooney & Duval, 1993] were used to empirically build sampling distributions for
the component loadings. This made it unnecessary to rely on theoretically derived
distributions, the values of which carry assumptions that may not be tenable for
the data from this study. These sampling distributions were then used to
construct confidence intervals for the loadings. Bootstrapped confidence intervals
were calculated with a combination of commercial statistical software (Systat)
and software designed for these data (Visual Basic) [Strube, 2002]. Resampling
for the bootstrapping procedure was carried out in the following way: In each of
the 2,000 bootstrapped samples, data from 52 animals were selected randomly
and with replacement. Eleven observation periods were selected randomly and
with replacement from the data for each of the 52 selected individuals. A
correlation matrix was constructed from the 11 observation periods for each
selected individual, and the 52 resulting matrices were then averaged, with each
correlation weighted by the available sample size. Occasionally the resampling
produced undefined correlates because there was no variability for a particular
variable. These instances were considered missing data, and the averaged
correlations were based on the remaining data. Occasionally some nondiagonal
correlations were equal to 1.0, a situation that prevents inversion of the matrix in
the PCA. These values were coded to .99 so the analysis could proceed. We
calculated the end points of the confidence intervals using the percentile method,
the bias-corrected percentile method, and the normal approximation method
[Mooney & Duval, 1993]. The bias-corrected method should have been the method
of choice for this analysis, but it did not appear to work well for these data.
Therefore, only those variables that loaded significantly on a component in both
the percentile and the normal approximation methods were considered significant
loadings. These two methods differ when the median bootstrap estimate is quite
different from the original parametric estimate, and requiring significance by
both methods is a conservative approach.
As a matter of interest, an R-type PCA with Varimax rotation was also
performed on the mean behavior/hour data from all monkeys in the study to
determine which components would emerge from this more traditional method of
analysis. In the R-type analysis, four components were judged as meaningful
according to the Scree test with sample data and a random data Scree test.
Confidence intervals were then constructed for the four R-type components by
means of the same method used in the P-type analysis.
Novel-Objects Study Data
To examine the relationships among behaviors in the novel-objects study and
the individual scores on Component 3, we calculated correlations for novel-object
186 / Rouff et al.
behaviors (bold, cautious, uninterested behaviors, and ‘‘curious’’ – bold +
cautious behaviors) with individual component scores for Component 3, as well as
with focal animal bold and cautious behaviors.
Multiple Regression Analysis
After the effects of age and zoo group were removed, the effects of sex,
hormone status, and dominance rank on the individual component scores from
the P-type PCA were investigated by means of a multiple regression analysis with
bootstrapped confidence intervals. To determine dominance status, we calculated
each individual’s rank within the social group using a dyadic interaction matrix
[Lehner, 1996] of assertive and submissive behaviors. The resulting linear ranks
were then divided into three equal groups (high-, mid-, and low-status).
Bootstrapped confidence intervals were calculated using the percentile-t
method [Mooney & Duval, 1993] with software designed for this project (Mathcad
2000 [Strube, 2001]). The bootstrapped sampling distribution was constructed
in the following way: For each of the 2,000 bootstrapped samples, a sample of 52
individuals was selected randomly and with replacement. The data for each
bootstrapped sample were then used in a standard multiple regression analysis to
produce the regression coefficients for each predictor in the regression model. The
resulting 2,000 sets of regression coefficients then constituted the bootstrapped
sampling distribution on which the confidence intervals were based. The second
resampling used to create the t-values was conducted on 100 samples from each
bootstrapped sample.
Three components, which accounted for 28.64% of the variance, were judged
as meaningful based on the Scree and random Scree tests. Details of the three
components are shown in Table III. Component 1 contained many Extraversion
behaviors and several Agreeableness behaviors. Sociable, affiliative, aloof,
welcome, approachable, and willing to interact loaded positively, and solitary
and slow loaded negatively at significant levels. Only one variableFagonistic–was
significantly related to Component 2. Bold and cautious behaviors loaded
positively on Component 3. As a matter of interest, the components from the
R-type analysis are presented in Table IV.
Bold, cautious, uninterested, or curious (bold + cautious) behaviors from the
novel-objects study were not related to the P-type Component 3 scores for
individuals. However, bold behaviors from the focal animal data were associated
with novel-object bold behaviors (r=.30, Po.05) and focal animal cautious
behaviors correlated with novel-object cautious behaviors (r=.41, Po.01).
The results of the multiple regression analysis indicated that the
mean component score for females was higher than that for males on the
Extraversion-like Component 1 (95% confidence interval for the mean difference=0.732–1.661) and on Component 3 (95% confidence interval for the
mean difference=0.105–0.699), after controlling the effects of age and
group. These differences can be seen in Fig. 1, which displays the simple
mean differences in component scores by sex prior to correction for age and
social group. Figure 2, which is also based on the simple mean component
scores, shows that the mean scores on Component 2, which was related
to a ‘‘disagreeable’’ behavior (agonistic), were greater for high-status
individuals relative to lower-ranked macaques (95% confidence interval for
Lion-Tailed Macaque Personality Traits / 187
TABLE III. P Type Principal Components
Sociable (E)
Solitary (E)
Affiliative (E)
Aloof (E)
Interacts (A)
Approachable (A)
Slow (E)
Welcome (A)
Active (E)
Persistent (P)
Agonistic (A)
Aggressive (A)
Sensitive (A)
Avoided (A)
Reactive (N)
Moves away (A)
Intervenes (A)
Relaxed (N)
Submissive (E)
Assertive (E)
Anxious (N)
Unreactive (N)
Cautious (NS) (E)
Bold (NS) (E)
Insensitive (A)
Eigenvalues for rotated components
Percent of total variance explained
Po.05 in bold.
E, Extraversion; N, Neuroticism; A, Agreeableness; NS, Novelty Seeking; P, Persistence.
the mean difference for mid-ranked=–0.203 to –0.984; low-ranked=–0.636 to –
Although the traits included in this study were drawn from previous
nonhuman primate personality studies and human models, the components that
resulted from the PCA reflected the relationships among the recorded behaviors
of the lion-tailed macaques in the study. Three components were judged as
meaningful, and bootstrapped confidence intervals provided the statistical
criteria for judging the significance of the behavioral categories to the
Component 1.
Component 1 contained behaviors that are consistent with the sociable
(gregariousness) and affiliative (warmth) facets of Extraversion. This component
emerged from all four preliminary PCAs of the subgroups, the analysis of all
animals, and the R-type analysis, which suggests that this component represents
188 / Rouff et al.
TABLE IV. R-Type Principal Components
Sociable (E)
Solitary (E)
Affiliative (E)
Slow (E)
Aloof (E)
Interacts (A)
Approachable (A)
Anxious (N)
Agonistic (A)
Submissive (E)
Aggressive (A)
Moves away (A)
Cautious (NS) (E)
Welcome (A)
Active (E)
Bold (NS) (E)
Assertive (E)
Reactive (N)
Self-groom (E)
Avoided (A)
Unreactive (N)
Sensitive (A)
Persistent (P)
Relaxed (N)
Insensitive (A)
Intervenes (A)
Eigenvalues of rotated components
Percent of total variance explained
Po.05 in Bold.
E, Extraversion; N, Neuroticism; A, Agreeableness; NS, Novelty Seeking; P, Persistence.
a robust dimension of lion-tailed macaque personality. The higher mean score for
females on Component 1 is consistent with the social organization of this femalebonded species. The results of this study suggest that the sociable and affiliative
personality traits of female lion-tailed macaques predispose them to engage in
interactions that establish and reinforce these social bonds. However, males
disperse from the natal group, and strong social bonds, fostered by social and
affiliative personality traits, may deter a male from leaving and/or interfere with
social factors that encourage emigration. A predisposition to socialize may also
distract resident males from directing vigilance behavior toward predators and
other rival males. Intermale aggression is not uncommon for this species, and
avoiding close proximity to others may reduce inadvertent conflicts and wounds.
Component 2.
The lower limits of the confidence interval for agonistic, the sole significant
loading, was close to zero with both the percentile and normal approximation
Lion-Tailed Macaque Personality Traits / 189
P Type Component Score Profiles By Sex
95% Confidence Intervals for the Mean
Component 1
Component 2
Component 3
Fig. 1. P-type component score profiles by sex, based on the simple mean scores for males
and females without correcting for age or group membership. The multiple regression indicated
that the mean score for females was significantly higher than the mean score for males on
Components 1 and 3.
methods. This provides weaker support for considering agonistic a significant
loading, and component 2 a significant component, compared to the evidence for
the other components. This component might be interpreted as the negative pole
of agreeableness, because agonistic and aggressive had the highest loadings on the
component (.50 and .49, respectively). It would not be surprising if a replication of
this study indicates that aggressiveness is also significantly related to this
component. Personality traits may be organized differently in different primate
species, and the negative aspects of Agreeableness may form a separate
personality dimension in some. Two other behavior-based personality studies
also found a separate aggressive/agonistic component (in chimpanzees [van Hooff,
1970] and rhesus monkeys [Chamove et al., 1972]). However, the relationships of
all the variables contribute to a component, not just the significant loadings. All
but one of the nine highest-loading variables on component 2 were behaviors
representing aspects of the Big Five Agreeableness factor.
It was an unexpected finding that all of these agreeable-like behaviors loaded
positively on this component, since this is not the bipolar pattern predicted by the
Big Five Model. It is possible that the behaviors representing these traits were not
truly bipolar opposites. However, the multiple regression procedure indicated a
relationship between this component and dominance status, with the mean score
of high-status individuals being greater than the mean scores of mid- or lowranking individuals. The pattern of higher agonistic, aggressive, sensitivity to
190 / Rouff et al.
P Type Component Profiles
By Dominance Rank
95% Confidence Intervals for the Mean
Component 1
Component 2
Component 3
Dominance Rank
Fig. 2. P-type component score profiles by dominance rank, showing that the simple uncorrected
mean score for high-ranking individuals was higher on Component 2 than the mean scores for
lower-ranking individuals.
group aggression, intervention, and reactivity behaviors seems consistent with
the behavior of more-dominant individuals, as does the attraction of the group to
them and the tendency of other group members to avoid dominant individuals
when approached by them. A dominance-related factor defined by assertiveness,
physical aggression, and low fearfulness was reported in seven out of 19 studies of
nonhuman animals [Gosling & John, 1999]. Neither assertive nor submissive
behaviors were related to this component, but these behaviors were narrowly
defined by priority of access. The highest loadings on Component 2 from the Rtype analysis were assertive, submissive, aggressive, moves away, and agonistic;
however, only submissive and agonistic were judged as significant. Clearly,
additional studies including more behavioral measures are required to determine
the exact nature of this component and whether it is best characterized as an
‘‘agreeable’’ component of both positive and negative traits, a ‘‘disagreeable’’
component, or perhaps even a dominance-related component.
Component 3.
Component 3 was unanticipated, because bold and cautious behaviors
(response to novelty) were relatively uncommon in the focal data. Bold and
cautious behaviors from the focal data both loaded positively on this component,
suggesting the trait of curiosity. Although Components 1 and 2 from the R-type
analysis appeared similar to the first two P-type Components, none of the R-type
components resembled P-type Component 3. P-type Component 3 is an example
Lion-Tailed Macaque Personality Traits / 191
of the different type of information gained when within-animal variance is
included with between-animal variance.
Component 3 scores for individuals were not correlated with bold, cautious,
uninterested, or curious (bold + cautious) behaviors in the novel-objects study,
even though focal bold behaviors were associated with novel-object bold
behaviors, and focal cautious behaviors were related to novel-object cautious
behaviors. This suggests that the component scores carry important (but as yet
unspecified) additional information from other variables beyond interest in
novelty. The Big Five Factor Model places a preference for novelty over
familiarity and intellectual curiosity on the openness to Experience factor.
Factors similar to the openness dimension were identified in studies of seven
nonhuman species [Gosling & John, 1999]. Curiosity is associated with
impulsiveness and recklessness in Cloninger’s [1994] Novelty Seeking factor
and Zuckerman et al.’s [1993] Impulsive Sensation Seeking. Impulsivity was not
included in this study, because no behaviors that covered all age/sex classes and
would be feasible in a zoo setting could be found to represent this trait. Adding
a measure of impulsivity might clarify whether Component 3 resembles either
the Openness to Experience Factor or Novelty/Sensation Seeking.
After the effects of age and group on Component 3 were controlled for, the
mean score for females was still significantly higher than the mean score for
males. The idea that males and females have different levels or types of curiosity
makes sense for this species in the wild. In the novel-objects study, the males were
interested in potentially threatening objects (the plastic owl, poster of a felid
head, and large mirror) but were less interested than the females in
nonthreatening items, such as pinwheels or small stuffed animals. Wild groups
should benefit if males, with their larger size and sharper canines, are
predisposed to remain vigilant and attend to potentially dangerous objects,
without being distracted by trivial novel items. However, higher levels of curiosity
would predispose females to investigate nonthreatening novel objects as well,
fostering the discovery of food resources to meet the high nutritional demands of
pregnancy, lactation, and infant care.
Critique of the method.
The method used in this study (i.e., behavioral observations used as data, a
P-type PCA, and bootstrapped confidence intervals as criteria for judging the
significance of factor loadings) provided an informative description of lion-tailed
macaque personality dimensions, and shows promise for both intra- and
interspecific personality studies. P-type Component 3 indicates that incorporating
both within- and between-individual variation in the P-type analysis, as used in
this study, can produce additional important information on personality.
The confidence intervals provided a more precise estimate of significance
compared to arbitrary rules of thumb. For example, a factor loading as low as 0.34
(welcome: P-type Component 1) was judged as significant, while a loading as
high as –0.69 (active: R-type Component 3) was rejected, and a 0.43 loading of
approachable on Component 1 was accepted, while a loading of 0.45 on
Component 2 for this behavior was not. Seven behaviors with loadings over
0.60 on the R-type components would have been incorrectly reported as
significant if an arbitrary 0.60 criterion for significance had been applied.
Granted, requiring significance by both the normal approximation and percentile
methods, as done in this study, is a stringent and conservative criterion; however,
it is not inappropriate for PCA, which capitalizes on chance. Most, but not all, of
192 / Rouff et al.
the excluded loadings over 0.60 were deemed significant by the normal
approximation method, which assumes that the data are normally distributed.
The results suggest that when a 0.60 cutoff is arbitrarily applied to non-normal
data, the reported results can be seriously misleading.
Bootstrapped confidence intervals for factor loadings appear to be an effective
method for decreasing the possibility that chance factor loadings will be
considered significant for both P- and R-type analyses, which is especially
important when sample sizes are small. However, the choice of a significance level
is also a somewhat arbitrary decision, and the expert eye of the researcher should
still determine when refinement and/or replication are needed to firm up
confidence in findings that are intriguing but do not currently meet the
traditional standard.
Some suggestions can be made for future studies that will use this method of
personality research. The traits included in the current study did not comprise an
exhaustive list, and traits that are important descriptors of lion-tailed macaque
personality may have been omitted. One could expand the trait list by consulting
researchers who are familiar with the species under study as to which traits to
add, and which behaviors represent the traits. A measure of impulsivity is
definitely needed, and Fairbanks’ [2001] intruder challenge test might be tried
when circumstances permit.
With regard to the persistence behaviors, the different circumstances at each
zoo resulted in different persistence opportunities for different groups, the
‘‘intermittent reward’’ may have had a different appeal for different monkeys,
and individuals with a low dominance rank may have had less access to
enrichment devices. Some traits, such as impulsivity and persistence, may be
better assessed under more controlled conditions and with experimental
In this study, continuous variables were dichotomized into two bipolar
opposites, such as sociable (within a body length) and solitary (farther than a body
length). Some traits, such as insensitive, were represented by several different
behaviors, some of which may have been stronger indicators of a trait than others.
Measuring continuous variables or behavioral categories comprised of multiple
behaviors on a five-point scale might better represent these traits.
The definitions of several behaviors representing Extraversion traits should
be redefined or removed from the ethogram in future studies. Aloof behaviors
were defined as refusing to play, refusing to groom another, or breaking off an
interaction. This required an individual to be sociable and possibly affiliative,
which resulted in a positive association of aloof with other positive Extraversion
behaviors, and not the negative association predicted by the Big Five Model.
Welcome and avoided behaviors were intended as a measure of how agreeable the
approached animal found the approaching individual. Avoided did not load
significantly on any component, and the relationship of welcome to other
extraversion behaviors most likely reflected an inclination to approach others.
One researcher coded nearly all but 20 hr of the lion-tailed macaque
behaviors observed in this study. The use of multiple coders would reduce the
influence of any bias from an individual’s idiosyncratic interpretation of
behavioral definitions or propensity to notice certain behaviors.
The P-type analysis used in this study integrated both within- and betweenindividual variation in behaviors. However, it also incorporated more variation
due to situation, relative to R-type analyses, which most likely explains why the
P-type components in this study accounted for much less of the variance (28.6%)
than the 58.7% in the R-type analysis from this study, and the 50–85% seen with
Lion-Tailed Macaque Personality Traits / 193
R-type components in previous personality studies. When using the P-type
analysis, it may be necessary to collect more sample periods for individuals than
the 24 per individual used in this study. This should improve the investigator’s
ability to estimate the P-type components, since similar situations would occur
with sufficient frequency that regular patterns of response would be detected,
increasing the amount of variance accounted for by the P-type components.
Although the method used in this study is well suited for comparative
research, specific comparisons can be problematic when analyses of the same
behavioral measures produce dissimilar components. Collecting trait term data in
the same manner used for behavioral data, using a defined list of trait terms and
behavior as an ethogram may help to resolve this problem. Behavioral measures
have been criticized as being unable to capture subtle, complex, or rarely
expressed traits, because they are limited to specific ethogram behaviors. Single
or yearly ratings with trait terms are vulnerable to memory biases. Combining
the best of both methods may compensate for the weakness of each technique
alone. When a researcher observes an animal running across a field, he/she relies
on the context to decide whether to code this motor action pattern as flight
behavior, aggression, or play. Coding a trait, such as gentle (responds in a kind
way, not rough or threatening), from a predefined adjective list should not be any
more subjective than collecting behavioral data. Trait term data should provide
information over and above what can be obtained from behavioral observations
alone. Adjective trait terms need not be converted into ‘‘personality’’ terms.
Therefore, simple frequencies could be compared, or the trait term data could be
subjected to a PCA or multiple regression analysis in the same manner used for
the behavioral data in this study. Although trait and behavioral data could be
analyzed separately, some questions might be best answered by a combination of
the two. Examining the correlations between trait terms and behavioral data
could clarify which behaviors best represent a trait, or, conversely, which
behaviors might be predicted from trait information. Physiological measures,
such as salivary cortisol or urinary hormone levels, should also be included in
these trait/behavior studies, when feasible, to investigate the biological bases of
behaviors and/or assessed traits.
By establishing personality dimensions and then validating them by
replication, one can establish ‘‘marker’’ variables, or traits that consistently load
highly on the same factor. Once a personality dimension has been validated,
component scores for new subjects can be calculated with unit weightings of
standardized behavioral frequency scores, provided that the behavioral categories
are defined in the same way [Figueredo & Ross, 1992]. These individual
component scores contain information on covariation as well as frequency of
behaviors. More narrowly focused questions could be investigated, such as the
relationship of a particular personality dimension to sex (as illustrated by Fig. 1)
or dominance status (Fig. 2), or to explore similarities and differences between
different primate groups. Differences in component score patterns and the
behavioral profiles that contribute to these scores could be examined on an
individual level (Fig. 3). Not only could between-individual comparisons be made,
but within-individual differences could be investigated with longitudinal studies
to consider the stability of personality and how component scores change from
infancy to adulthood or in response to changing social environments. Species
could be compared by examining the similarities and differences in the
194 / Rouff et al.
Response to Novelty and Persistence Behaviors
P Type Component Scores
Component 1
Component 2
Component 3
95% Confidence Interval for the Mean/Sample
Extraversion-Like Behaviors
Extraversion-like Behaviors
Affiliativeness and Dominance Facets
Proximity and Activity Facets
95% Confidence Interval for the Mean/Sample
95% Confidence Interval for the Mean/Sample
Refuse to Play/Groom
Agreeableness-Like Behaviors
Moves away
95% Confidence Interval for the Mean/Sample
95% Confidence interval for the Mean/Sample
Agreeableness-Like Behaviors
Breaks Off
Fig. 3. P-type component score profile and behavioral profiles for Tofu, a low-ranking female in
the St. Louis Zoo family group, who was born in 1981.
components that emerge from the analysis of behavioral or trait data, provided
that equivalent species-specific measures are used.
Comparative studies on primate personality traits can be achieved only
through a long-term coordinated effort that combines the results from different
Lion-Tailed Macaque Personality Traits / 195
sites and investigators, which makes the use of standardized measures essential
[de Waal & Luttrell, 1989; Gosling, 1999, 2001; Moore, 1992, 1993]. Research on
personality traits should benefit if researchers studying different topics and
species would collaborate by adding standardized trait term data to their research
design, and by collecting trait data in the same manner used for behavioral data.
Therefore, the development of a taxonomy of standardized trait terms that is
applicable to a variety of primate species, and considers the different ways a trait
might be manifested by different species is critical [Gosling, 1999, 2001].
We thank Jane Phillips-Conroy, C. Robert Cloninger, Tab Rasmussen,
Richard Smith, and Samuel Gosling for sharing their expertise and comments.
We are most grateful to the reviewers of the manuscript for their valuable
insights and suggestions. We also thank the Baltimore Zoo, CRES, the Mesker
Park Zoo, and the St. Louis Zoo (especially Ingrid Porton, Nancy Harvey, Karen
Barnes, Kathy de Falco, Mike Cranfield, Laura Campbell, Joanne Olivia-Purdy,
Gwen Mullen, and Syd Dawson) for their assistance and warm hospitality.
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