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The effect of the owner's personality on the behaviour of owner-dog dyads


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We describe the relationships between dog owners' personality attributes (assessed via questionnaire), their behaviours and the dog's behaviours observed during brief dog-owner and dog-stranger interactions (N = 78). Interactions comprised the owner commanding the dog to sit, and the stranger showing a ball to the restrained dog and then hiding it. Owners scoring higher on neuroticism and openness used more commands (gestural and verbal) when asking the dog to sit, and the dogs of owners higher on neuroticism obeyed with a longer latency and spent more time looking at the stranger. More extraverted owners praised their dog more, and it took longer for their dogs to look at the stranger but they spent more time looking at the stranger, whereas dogs of more agreeable owners spent more time looking at the ball. Based on these results we conclude that some aspects of owners' personality appear to be tied to their dog's attentional concerns.
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Interaction Studies 13:3 (2012), . doi 10.1075/is.13.3.03kis
issn 15720373 e-issn 15720381 © John Benjamins Publishing Company
e eect of the owner’s personality on the
behaviour of owner-dog dyads
Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
Eötvös Loránd University, Hungary
We describe the relationships between dog owners’ personality attributes
(assessed via questionnaire), their behaviours and the dogs behaviours observed
during brief dog-owner and dog-stranger interactions (N = 78). Interactions
comprised the owner commanding the dog to sit, and the stranger showing a ball
to the restrained dog and then hiding it. Owners scoring higher on neuroticism
and openness used more commands (gestural and verbal) when asking the dog
to sit, and the dogs of owners higher on neuroticism obeyed with a longer latency
and spent more time looking at the stranger. More extraverted owners praised
their dog more, and it took longer for their dogs to look at the stranger but they
spent more time looking at the stranger, whereas dogs of more agreeable owners
spent more time looking at the ball. Based on these results we conclude that
some aspects of owners’ personality appear to be tied to their dog’s attentional
Keywords: dog-human interaction; personality; multivariate statistical methods
. Introduction
. Dog-human relationship
Humans engage in heterospecic interactions with a variety of agents ranging
from dierent animal species (e.g. Podberscek, Paul & Serpell 2000; Robinson
1995) to social robots (run 2004). Among these interactions the perhaps most
widely studied one is the human-dog interaction.
Dogs are among the most popular pets in the western world (Hart 1995) and
are present in almost every human society worldwide (Serpell 2003). ey have
evolved specialized skills for reading human social and communicative behaviour,
which enabled them to perform tasks to assist humans (e.g. the comprehension
of human pointing gestures is a basic skill in assistance dogs or following human
gaze is useful in everyday cooperative situations) (Cooper 2003; Hare & Tomasello
2005; Miklósi, Topál & Csányi 2004). Dogs show attachment to their owner (Topál,
Miklósi, Csányi & Dóka 1998; Prato-Previde, Custance, Spiezio & Sabatini 2003)
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
that is, they have a special aectional relationship based on dependency between
individuals that becomes evident through behavioural preferences (Wickler 1976).
Furthermore they are considered to be a promising model species for studying sev-
eral complex phenomena such the genetic basis of certain human illnesses (Over-
all 2000) or human-robot interaction (Syrdal, Koay, Gácsi, Walters & Dautenhahn
2010; Miklósi & Gácsi 2012).
. Dog-human interaction and the role of personality
Owners keep and use their dogs for dierent purposes and also marked varia-
tion exists in the relationship between owners and their dogs (Hart 1995). ere
are highly coordinated owner-dog units, such as blind owners and their guide
dogs (Naderi, Miklósi, Dóka & Csányi, 2001), while there are dogs that would not
even reliably return when called (Serpell 1996). Associations have been reported
between the owners’ and dogs’ personality. For instance, owners of highly aggres-
sive English cocker spaniels tend to be emotionally less stable, shy, undisciplined
and more likely to be tense than owners of low aggressive spaniels (Podberscek
& Serpell 1997). Owners also showed some degree of similarity with their dog in
their personality prole (Turcsán, Kubinyi, Virányi & Range 2011).
Several studies have already investigated the interaction of human-dog dyads
in situations like interspecic play (Mitchell & ompson 1986, 1990, 1991;
Rooney, Bradshaw & Robinson 2001) and problem solving tasks (e.g. Topál, Miklósi
& Csányi 1997). Other studies using “eld-based” methodology and focusing on
the aspects related to dog-training (e.g. Braem & Mills 2010; Fukuzawa, Mills &
Cooper 2005) found that varying the way an experimental trainer communicates
(e.g. posture, eye-contact) with the dog when giving simple commands like “come
and “sit” inuences the obedience of the dogs.
Despite the extended literature on dog-human relationships, only a little
is known about the eects of the owners’ personality on the dog-owner dyadic
interaction. It has been reported that the higher the owners score in neuroti-
cism, the more they consider their dog a social supporter which is related to
a low dyadic functionality (e.g. they engage less in shared activities with the
dog) (Kotrschal, Schöberl, Bauer, ibeaut & Wedl 2009). In contrast, the higher
owners scored in extraversion, the less they tended to consider their dogs as
social supporters and the more these owners appreciated shared activities with
their dogs. However, the authors noted that due to the low sample size (N = 22)
the results need to be interpreted cautiously. Data on the same subjects was later
published with a slightly dierent focus (Wedl, Schöberl, Bauer, Day & Kotrschal
2010) concluding that the personality of the owners and dogs, the nature of the
human-dog attachment, and the owner-dog relationship (e.g. shared activity)
may inuence dogs’ social attraction to their owners.
e eect of the owner’s personality on the behaviour of owner-dog dyads 
. Aims of the study
In the present paper we aim to give a detailed behavioural analysis of the human-
dog interaction in a short series of simple actions observing a large number of
human-dog dyads. Behavioural observations were complemented by the measure-
ment of human personality and some general information (including dog keeping
practices). Our objective was to examine consistent relationships in the behav-
iours of dogs and their owners in interaction with each other or a stranger, and to
discern inuences of owner personality on dog behaviour by means of multivariate
statistical methods.
. Material and methods
. Subjects
A total of 78 dog-owner pairs participated in the experiment. Owners from a data-
base containing approximately six hundred volunteers were contacted in alpha-
betical order and they took part in the study if their dog could be described as
motivated to play with a ball” and they themselves were willing to participate in
the experiment. e test was conducted in the Clever Dog Lab, Vienna from July
to September 2009. Owners were 14 males and 64 females, all older than 18 years
old with an average age of 43.8 (±19.0) years. Dogs were 40 males and 38 females
from 27 dierent breeds and 15 mongrels. ey were all older than one year with
an average age of 4.2 (±2.6) years. Some of the owner-dog pairs had previously
participated in other behaviour tests but all of them were naive to the current
experiment. All tests were carried out by the same 22 year-old female, who was
unfamiliar to all subjects.
. Procedure
To assess the human personality we used the German version of the Big Five
Inventory (BFI, John & Srivastava 1999) translated and validated by Lang, Lüdtke
and Asendorpf (2001), measuring neuroticism, extraversion, openness, agree-
ableness and conscientiousness. Neuroticism refers to the tendency to be anxious,
insecure, and self-pitying versus calm, secure, and self-satised. Extraversion
refers to the tendency to be sociable, fun-loving, and aectionate versus retiring,
somber, and reserved. Openness refers to the tendency to be imaginative, inde-
pendent, and interested in variety versus practical, conforming, and interested
in routine. Agreeableness refers to the tendency to be sohearted, trusting, and
helpful versus ruthless, suspicious, and uncooperative. Conscientiousness refers
to the tendency to be organized, careful, and disciplined versus disorganized,
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
careless, and impulsive. e questionnaire consisted of 44 items (e.g. “I see myself
as someone who is sometimes shy, inhibited) and the owners had to rate them-
selves on each item using a ve-point interval scale (disagree strongly – agree
Aer the owners completed the questionnaire the dog and the owner entered
the test room (6.3 m × 4.8 m) together with the female experimenter (E). e test
consisted of two phases where we observed human-dog interaction with the owner
and the experimenter, respectively. We applied two short scenarios that resem-
bled everyday life events. First the dog had to accomplish a simple and already
known command that was given by the owner in a somewhat novel context. en
a stranger manipulated a ball calling the dogs attention to her actions in a social
learning-like communicative context.
In the rst test phase (duration: 37.3 ± 8.2 s) the owner was instructed to
make the dog sit in the middle of the room as he/she usually does and to walk
around the room while the dog was expected to stay in the same place. Aer walk-
ing around, the owner returned to the dog and was instructed to hold the dogs
collar (video: en in
the second phase, (duration: 19.6 ± 2.1 s) the E placed an opaque screen (30 cm
wide x 50 cm high x 30 cm deep) and a tennis ball 2 m from the dog and 1 m from
each other. First E called the dog to get its attention while standing next to the
dog-owner pair, then she walked to the ball without looking at the dog. E picked
the ball up, and said “Schau mal!” (the German equivalent of “Look!”) to the dog.
Next she walked to the screen and hid the ball behind it, then walked back to
the subject showing her empty hands (video:
Both phases were videotaped with a four-camera-system for later analysis.
. Data analysis
Five behaviour variables were analysed to describe the dogs’ reactions during
the interactions. In Phase 1 we measured the Latency of accomplishing the com-
mand from the moment when the dog-owner pair entered the room and the
Time spend looking at owner from the moment when the dog took the sitting
position. In Phase 2 the Latency to look at the experimenter, Time spend looking
at the experimenter and Time spend looking at the ball was measured from the
moment when the experimenter called the dog. We also recorded the num-
ber and type of the commands the owners used in Phase 1 (Table 1). A Verb
was dened as an utterance containing a single verb (e.g. “Sitz!” “Bleib!”, that
is the German equivalent of “Sit!” “Stay!”); an Attention getter contained the
dogs’ name and/or the utterance “Schau mal!” (“Look!”); a Praise was a positive
e eect of the owner’s personality on the behaviour of owner-dog dyads 
utterance such as “Super!” or “Gut gemacht!” (“Great!” “Well done!”). We also
calculated the Total verbal information that was the sum of Verbs, Attention
getters and Praises. A Hand sign was dened as a voluntary hand movement
directed towards the dog.
Table 1. Variables used in the present study (with the abbreviations in parenthesis where
applicable) and the reliability measures in the case of behavioural variables
Source Dog Owner
Phase 1
Latency of accomplishing the
command (LatSit), κ = 1
Total verbal information
Time spend looking at owner
(LookOwn), κ = 0.9
Verbs, κ = 0.89
Attention getters, κ = 0.9
Praise, κ = 0.9
Hand signs, κ = 0.85
Phase 2
Latency to look at the exp.
(LatLookExp), κ = 0.8
Time spend looking at the exp.
(LookExp), κ = 0.8
Time spend looking at the ball
(LookBall), κ = 0.8
Behavioural variables were coded with frame-by-frame inspection of the
recordings using Solomon Coder (© András Péter,,
a widely used behaviour coding soware (e.g. Horn, Virányi, Miklósi, Huber &
Range 2011; Marshall-Pescini, Passalacqua, Barnard, Paola Valsecchi & Prato-
Previde 2009). Reliability measures (Cohens Kappa) for both phases were obtained
by coding of 20 videos. According to the categorization by Landis and Koch (1977)
almost perfect agreement (0.81–1) was found for all variables. e personality
questionnaire was evaluated only aer the behaviour test.
Based on these data we gave a multivariate description of the dyads’ interac-
tion using Redundancy Analysis (RDA, Wollenberg 1977). Behaviour variables
from both the owner and the dog were entered into the same statistical model and
the owners personality factors were used as explanatory variables. is method
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
is suitable to qualitatively analyze data sets where the explanatory variables (in
this case the owners’ personality) are supposed to inuence the other variables
(in this case the behavioural variables coded in the two test phases) without the
latter having an inuence on them. Although human personality might depend
on several factors, based on a denition by Funder et al. (1997) suggesting that
personality is stable across time and situations, for this analysis we expected that
the owners’ personality was not inuenced by the dogs’ behaviour. We further
assumed that as the owner may actively choose the breed or individual (s)he
wants to live with, in this way (s)he might be able to impact on the behaviour of
his/her dog.
For statistical analysis we used Syntax 2000 (© János Podani, http://ramet, a widely used multivariate statistical analysis
soware (e.g. Altobelli, Bressan, Feoli, Ganis & Martini 2006; Bourgeois, Kenkel
& Morrison 1997).
. Results
In order to give a general picture of what happened in the two phases of the test,
rst we provide descriptive results (average and SD). During the rst test phase
the owners used 2.5 (±1.9) hand signs and 9.7 (±7.6) pieces of verbal informa-
tion out of which 6.6 (±4.8) were verbs, 1.9 (±2.0) were attention getters and
0.7 (±1.2) were praise. e dogs needed 18.7 (±14.7) seconds to accomplish the
Sit!” command, and they were looking at the owner 86.4 (±13.5) % of the time.
In the second phase, the dogs looked at the experimenter with a mean latency
of 0.55 (±0.70) second when she called their attention. e dogs were looking
at the experimenter 66.2 (±28.3) % of the time, and at the ball 28.1 (±27.6) % of
the time.
An RDA was carried out on data gathered from the interaction test with the
owners personality factors as explanatory variables. Owner-dog pairs were there-
fore positioned in an N dimensional space (with N being the number of axes)
according to both the owners’ and the dogs’ behaviour. e axes are expressed
in arbitrary units and were similarly derived as those of a Principal Component
Analysis (PCA), that is data reduction method was used to decrease the number of
axes/dimensions by reducing the number of variables through computing behav-
ioural factors containing more than one variable. e analysis results in a treeplot
(Figure 1) where the rst two dimensions/axes (the ones with most explained
variance) are plotted with the two axes representing behavioural factors expressed
in arbitrary units. e two canonical RDA axes explained 70% of the total vari-
ance (for comparison see ecological studies using the same method: e.g. Tinya,
e eect of the owner’s personality on the behaviour of owner-dog dyads 
rialigeti, Király, Németh & Ódor 2009). In order to make visible the behav-
ioural variables which constitute the factors, the variables are also plotted (black
circles) and labeled on the gure. A bigger distance from zero means a bigger load
on the factor. Each dog-owner pair is plotted according to their values for the two
behavioural factors (axis 1 and 2).
Axis 2
Axis 1
–16 –14 –12 –10 –8 –6 –4 –2 0 2 4 6 8 10 12 14 16
Figure 1. Treeplot showing the results of the Redundancy Analysis. e light grey squares
are the individual dog-owner pairs, the black circles are the behavioural variables observed in
Phases 1 and 2 of the social interaction test and the dark grey triangles represent the personality
factors of the owners. e black circles, which appear close to the lines connecting the triangles
to the point of zero, indicate close association. Variable abbreviations are provided in Table 1
Explanatory variables (personality factors of the owner) are plotted according
to their relatedness to axes 1 and 2 (gray triangles). e visual examination of the
treeplot (Figure 1) showed that the rst axis (explained variance 42%) was associ-
ated positively with the owners’ scores on extraversion and negatively with the
scores on agreeableness. e second axis (explained variance 28%) was associated
positively with the owners’ scores on openness and negatively with the scores on
conscientiousness. Owners’ neuroticism was associated positively with both axes
to some extent.
e treeplot of the RDA provides information also on the relationship between
the owners’ personality traits and the behaviour of the dyads; the physical distance
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
between the lines connecting the personality factors (plotted as gray triangles) to
the origo and the behavioural variables (plotted as black dots) means relatedness.
Owners’ neuroticism was associated with the dogs’ accomplishing the “Sit!” com-
mand with a longer latency and looking more to the experimenter in the second
phase. Furthermore, neuroticism and openness traits were positively related to the
number of hand signs and commands the owners used in the rst test phase (total
verbal information, attention getters, and verbs). e owners’ extraversion trait
was also related to the number of times the owners praised the dog in this phase.
Moreover, owners who rated themselves higher on extraversion had dogs which
looked with a longer latency at the experimenter and spent more time looking at
her in the second test phase. Dogs with highly agreeable owners spent more time
looking at the ball in the second test phase.
. Discussion
. Analysing dog-human interaction
In the present paper we described the social interaction of dog-human dyads while
accomplishing simple tasks and revealed that the owners’ personality relates to the
observed behaviour in dogs and their owners. e main advantage of the present
study is that the use of a multivariate method allowed us to describe the interaction
of a large number of owner-dog dyads by the means of a single statistical model.
ese descriptive statistics are widely used in ecological studies (e.g. Guisan 2000)
where a lot of eld data are available in order to give a unied description of the
whole study area. However, behaviour observations carried out with relatively low
sample sizes are usually analyzed with univariate methods (although see Everitt
2009 for multivariate analysis of behavioural data) focusing on only one variable
in each statistical test.
It has already been proposed that owner-dog dyads might function as one unit
(Mitchell & ompson 1991; Naderi et al. 2001), for example due to a common
goal. We suggest that there is a variation to what extent owner-dog dyads form a
unit. In the present study we showed that dyadic behaviour can be studied not only
in complex situations (such as playing or mastering an obstacle course) but also in
a very simple situation.
. e eect of the owners’ personality on dog-human interaction
It was reported earlier (Kotrschal et al. 2009) that the higher the owners score in
neuroticism, the greater their attachment was to their dogs. In parallel we revealed
e eect of the owner’s personality on the behaviour of owner-dog dyads 
that owners scoring higher in neuroticism use more commands and hand signs
when making the dog perform a simple obedience task (sit and stay) which might
be a sign of social relatedness (Furrer & Skinner 2003). e close social relation-
ship of owners with their dogs associated with neuroticism was also reported to
be linked to low dyadic functionality by Kotrschal et al. (2009). Similarly we found
that higher scores on neuroticism in owners were also related to longer latencies
when accomplishing the “Sit!” command in dogs. Braem and Mills (2010) found
also that with the handler giving additional verbal information besides the com-
mand (that is comparable with the total verbal information in our test), the dog’s
obedience decreased (comparable to latency of accomplishing the command in
the present experiment).
Owners scoring high in extraversion seem to have more extraverted dogs
according to a questionnaire survey (Turcsán, Kubinyi, Virányi et al. 2011).
Similarly, we found that the owners’ extraversion was positively associated with
the dogs’ looking at the owner in the rst test phase and their looking at the
experimenter in the second phase, while less social behaviours like looking at
the ball were negatively related to this personality trait. However, we also found
previously unreported connections of the owners’ openness and agreeable-
ness to the dog and owner behaviour: the owners’ openness trait was positively
related to the number of hand signs and commands they used in the rst test
phase (total verbal information, attention getters, and verbs) while dogs with
highly agreeable owners spent more time looking at the ball in the second test
. Dog-owner interaction in a broader sense
Similarly to other ndings (Kotrschal et al. 2009; Turcsán, Kubinyi, Virányi et al.
2011; Wedl et al. 2010) we found a relationship between the behaviour of owners
and their dogs in many aspects.
Mitchell and Edmonson (1999) described how owners talk to their dogs in
a play situation. ey found that many of them “chatted” to their dogs in quite
a complex way using repetitive talk. Similarly, we found that owners in this con-
text used imperatives (verbs) and attention getters the most frequently during the
It is also important to point out that, as we have seen, the owners’ person-
ality has an impact on how the dogs behave, which might also bias the results
of such cognitive tests where the owners are allowed to participate actively
(e.g. Elgier, Jakovcevic, Mustaca & Bentosela 2009; Prato-Previde, Marshall-
Pescini & Valsecchi 2008).
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
. Conclusion
In sum, the present paper provided a qualitative description of associations
between the owners’ personality and the behaviour of owner-dog dyads during
a simple interaction task. We found that the owners’ personality inuenced the
dyads performance: neurotic owners used more commands and their dogs obeyed
with a longer latency; extroverted owners used more praises and their dogs spent
more time looking at the experimenter while dogs of agreeable owners spent more
time looking at the ball. ese results might contribute to our understanding of
human-companion relationships in a broader sense.
We would like to thank Zsóa Virányi and Friederike Range for hosting this experiment in
the Clever Dog Lab and Tamás Fara, Robert W. Mitchell and ve anonymous reviewers for
useful comments on a previous version of the manuscript. e research was supported by the
LIREC project (FP7-215554), MTA 01 031 and by the ETOCOM project (TÁMOP-4.2.2-08/1/
KMR-2008-0007) through the Hungarian National Development Agency in the framework
of Social Renewal Operative Programme supported by EU and co-nanced by the European
Social Fund.
Altobelli, A., Bressan, E., Feoli, E., Ganis, P., & Martini, F. (2006). Digital representation of
spatial variation of multivariate landscape data. Community Ecology, 7(2), 181–188.
doi: 10.1556/ComEc.7.2006.2.5.
Bourgeois, L., Kenkel, N.C., & Morrison, I.N. (1997). Characterization of cross-resistance pat-
terns in aceryl-CoA carboxylase inhibitor resistant wild oat (Avena fatua). Weed Science,
45, 750–755.
Braem, M.D., & Mills, D.S. (2010). Factors aecting response of dogs to obedience instruc-
tion: A eld and experimental study. Applied Animal Behaviour Science, 125(1–2), 47–55.
doi: 10.1016/j.applanim.2010.03.004.
Cooper, J. (2003). Clever hounds: Social cognition in the domestic dog (Canis familiaris).
Applied Animal Behaviour Science, 81(3), 229–244. doi: 10.1016/S0168-1591(02)00284-8.
Elgier, A.M., Jakovcevic, A., Mustaca, A.E., & Bentosela, M. (2009). Learning and owner-
stranger eects on interspecic communication in domestic dogs (Canis familiaris). Behav-
ioural Processes, 81(1), 44–9. doi: 10.1016/j.beproc.2008.12.023.
Everitt, B.S. (2009). Multivariable modeling and multivariate analysis for the behavioral sciences
(pp. 1–304). CRC Press.
Fukuzawa, M., Mills, D.S., & Cooper, J. (2005). More than just a word: Non-semantic command
variables aect obedience in the domestic dog (Canis familiaris). Applied Animal Behaviour
Science, 91(1–2), 129–141. doi: 10.1016/j.applanim.2004.08.025.
e eect of the owner’s personality on the behaviour of owner-dog dyads 
Funder, D., John, O.P., Robins, R., & Pervin, L. (1997). Handbook of personality (2nd ed.).
New York: Guilford Press.
Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in childrens academic
engagement and performance. Journal of Educational Psychology, 95(1), 148–162.
doi: 10.1037/0022-0663.95.1.148.
Guisan, A. (2000). Predictive habitat distribution models in ecology. Ecological Modelling,
135 (2–3), 147–186. doi: 10.1016/S0304-3800(00)00354-9.
Hare, B., & Tomasello, M. (2005). Human-like social skills in dogs? Trends in Cognitive Sciences,
9(9), 439–44. doi: 10.1016/j.tics.2005.07.003.
Hart, L.A. (1995). Dogs as human companions: A review of the relationship. In J.A. Serpell
(Ed.), e domestic dog: Its evolution, behaviour and interactions with people (pp. 161–178).
Cambridge: Cambridge University Press.
Horn, L., Virányi, Z., Miklósi, Á., Huber, L., & Range, F. (2012). Domestic dogs (Canis familiaris)
exibly adjust their human-directed behavior to the actions of their human partners in a
problem situation. Animal Cognition, 15(1), 57–71. doi: 10.1007/s10071-011-0432-3.
John, O.P., & Srivastava, S. (1999). e big-ve trait taxonomy: History, measurement, and theo-
retical perspectives. Handbook of Personality: eory and Research.
Kotrschal, K., Schöberl, I., Bauer, B., ibeaut, A.-M., & Wedl, M. (2009). Dyadic relationships
and operational performance of male and female owners and their male dogs. Behavioural
Processes, 81(3), 383–91. doi: 10.1016/j.beproc.2009.04.001.
Landis, J.R., & Koch, G.G. (1977). e measurement of observer agreement for categorical data.
Biometrics, 33, 159–174.
Lang, F.R., Lüdtke, O., & Asendorpf, J.B. (2001). Validity and psychometric equivalence of
the German version of the Big Five Inventory in young, middle-aged and old adults.
Diagnostica, 47, 111–121.
Marshall-Pescini, S., Passalacqua, C., Barnard, S., Valsecchi, P., & Prato-Previde, E. (2009). Agil-
ity and search and rescue training dierently aects pet dogs’ behaviour in socio-cognitive
tasks. Behavioural Processes, 81(3), 416–22. doi: 10.1016/j.beproc.2009.03.015.
Miklósi, Á., Topál, J., & Csányi, V. (2004). Comparative social cognition: What can dogs teach
us? Animal Behaviour, 67(6), 995–1004. doi: 10.1016/j.anbehav.2003.10.008.
Miklósi, Á., & Gácsi, M. (2012). On the utilisation of social animals as a model for social robot-
ics. Frontiers in Psychology, 3, 1–10. doi: 10.3389/fpsyg.2012.00075.
Mitchell, R.W., & Edmonson, E. (1999). Functions of repetitive talk to dogs during play: Control,
conversation, or planning? Society and Animals, 7(1), 55–81. BRILL.
Mitchell, R.W., & ompson, N.S. (1986). Deception in play between dogs and people. In R.W.
Mitchell & N.S. ompson (Eds.), Deception. Perspectives on human and nonhuman deceit
(pp. 193–205). State University of New York Press.
Mitchell, R.W., & ompson, N.S. (1990). e eects of familiarity on dog-human play.
Anthrozs, 4(1), 24–43. Berg Publishers.
Mitchell, R.W., & ompson, N.S. (1991). Projects, routines, and enticements in dog-human
play. In P.P.G. Bateson & P.H. Klopfer (Eds.), Perspectives in ethology Vol. 9 human under-
standing and animal awareness (pp. 189–216). New York and London: Plenum Press.
Naderi, S., Miklósi, Á., Dóka, A., & Csányi, V. (2001). Co-operative interactions between blind
persons and their dogs. Applied Animal Behaviour Science, 74(1), 59–80. doi: 10.1016/
Overall, K.L. (2000). Natural animal models of human psychiatric conditions: Assessment of
mechanism and validity. Progress in Neuro-Psychopharmacology and Biological Psychiatry,
24(5), 727–776.
 Anna Kis, Borbála Turcsán, Ádám Miklósi & Márta Gácsi
Podberscek, A.L., & Serpell, J.A. (1997). Aggressive behaviour in English cocker spaniels and the
personality of their owners. Veterinary Record, 141, 73–76.
Podberscek, A.L., Paul, E., & Serpell, J.A. (2000). Companion animals and us: Exploring the
relationships between people and pets (pp. 1–335). Cambridge, UK: Cambridge University
Prato-Previde, E., Custance, D.M., Spiezio, C., & Sabatini, F. (2003). Is the dog-human rela-
tionship an attachment bond? An observational study using Ainsworths strange situation.
Behaviour, 140, 225–254.
Prato-Previde, E., Marshall-Pescini, S., & Valsecchi, P. (2008). Is your choice my choice? e
owners’ eect on pet dogs' (Canis lupus familiaris) performance in a food choice task.
Animal Cognition, 11(1), 167–74. doi: 10.1007/s10071-007-0102-7.
Robinson, I. (1995). e waltham book of human-Animal interaction: Benets and responsibilities
of pet ownership (pp. 1–148). Kidlington: Pergamon Press.
Rooney, N.J., Bradshaw, J.W.S., & Robinson, I.H. (2001). Do dogs respond to play signals given
by humans? Animal Behaviour, 61(4), 715–722. doi: 10.1006/anbe.2000.1661.
Serpell, J.A. (1996). Evidence for an association between pet behavior and owner attachment lev-
els. Applied Animal Behaviour Science, 47 (1–2), 49–60. doi: 10.1016/0168-1591(95)01010-6.
Serpell, J.A. (2003). Anthropomorphism and anthropomorphic selection-Beyond the “Cute
Response.Society and Animals, 11(1), 83–100. doi: 10.1163/156853003321618864.
Syrdal, D.S., Koay, K.L., Gácsi, M., Walters, M.L., & Dautenhahn, K. (2010). Video prototyp-
ing of dog-inspired non-verbal aective communication for an appearance constrained
robot. 9th IEEE International Symposium on Robot and Human Interactive Communication.
Viareggio, Italy.
run, S. (2004). Toward a framework for human-robot interaction. Human-Computer Interac-
tion, 19(1), 9–24. doi: 10.1207/s15327051hci1901&2_2.
Tinya, F., Márialigeti, S., Király, I., Németh, B., & Ódor, P. (2009). e eect of light conditions
on herbs, bryophytes and seedlings of temperate mixed forests in Őrség, Western Hungary.
Plant Ecology, 204(1), 69–81. doi: 10.1007/s11258-008-9566-z.
Topál, J., Miklósi, Á., & Csányi, V. (1997). Dog-human relationship aects problem solving
behavior in the dog. Anthrozoös, 10(4), 214–224. doi: 10.2752/089279397787000987.
Topál, J., Miklósi, Á., Csányi, V., & Dóka, A. (1998). Attachment behavior in dogs (Canis familia-
ris): A new application of Ainsworths (1969) Strange Situation Test. Journal of Comparative
Psychology, 112(3), 219–229. doi: 10.1037/0735-7036.112.3.219.
Turcsán, B., Kubinyi, E., Virányi, Z., & Range, F. (2011). Personality matching in owner-dog
dyads. Journal of Veterinary Behavior: Clinical Applications and Research, 6(1), 77. doi:
Wedl, M., Schöberl, I., Bauer, B., Day, J., & Kotrschal, K. (2010). Relational factors aecting
dog social attraction to human partners. Interaction Studies, 11(3), 482–503. doi: 10.1075/
Wickler, W. (1976). e ethological analysis of attachment: Sociometric, motivational and
sociophysiological aspects. Zeitschri für Tierpsychologie, 42(1), 12–28.
Wollenberg, A.L. (1977). Redundancy analysis an alternative for canonical correlation analysis.
Psychometrika, 42(2), 207–219. doi: 10.1007/BF02294050.
e eect of the owner’s personality on the behaviour of owner-dog dyads 
Authors’ addresses
Anna Kis (corresponding author)
Eötvös Loránd University
Department of Ethology
H-1117 Pázmány P. s. 1/c Budapest
Borbála Turcsán
Eötvös Loránd University
Department of Ethology
H-1117 Pázmány P. s. 1/c Budapest
Ádám Miklósi
Eötvös Loránd University
Department of Ethology
H-1117 Pázmány P. s. 1/c Budapest
Márta Gácsi
Department of Ethology
H-1117 Pázmány P. s. 1/c Budapest
Authors’ biography
Anna Kis joined the Family Dog Project of the Department of Ethology at the Eötvös University
in 2007 where in 2010 she obtained her BSc degree in biology studying human-directed
aggression in dogs. During her studies she also gained research experience in the Department of
Cognitive Biology of the University of Vienna and in the Konrad Lorenz Institute at Altenberg
on the topic of “A-not-B” error in dogs and marmosets. Her current research interest focuses on
dog-human interaction and etorobotics.
Márta Gácsi is a post-doctorate researcher of the Family Dog Project at Eötvös University. She
gained her Ph.D. in 2003 on the attachment of dogs towards their owners. Since then she has
been supervising several graduate and undergraduate students on various topics. Her current
research interest focuses on dog aggression, attachment toward the owner, dog-human com-
munication and etorobotics.
Borbála Turcsán is a Ph.D. student at the Department of Ethology at Eötvös University. She
graduated at the same university in 2009 with a thesis about species typical behaviour in dogs.
She has been a member of the Family Dog Project for ve years and has participated in several
projects concerning the personality and behavioural genetics of dogs. She also spent ve months
in the Clever Dog Lab in Vienna. Her current research interest focuses mainly on dog and owner
personality and its relation to dog-owner relationship.
Ádám Miklósi has been the head of the Department of Ethology at Eötvös University since
2006 and at the same time leader of the Family Dog Project. He graduated at the same university
in 1986 as a biologist and obtained his Ph.D. in 1995. His current research interest focuses on
the ethology of dogs including several subdisciplines from behavioural genetics through social
cognition to etorobotics. In 2008 he published a book entitled Dog Behaviour, Evolution, and
Cognition at Oxford University Press.
... Canine factors identified to affect the human-dog relationship include morphological traits (7), age (8), breed (9,10), and behaviour (9). For humans, influencing factors include training techniques used (11), personality (12) and other demographic factors [e.g., gender (13) and socioeconomic status (14)]. ...
... Different types of people interact differently with dogs (12,15,16). More educated people better identify the signs of stress in dogs (17); use more reward-based training techniques (16) and develop closer relationships with their dogs (18). ...
... Statements 2,4,6,7,8,10, and 12 require reverse scoring. Volunteer talks to the dog with an utterance containing a single command (e.g., "Stay!" "Come!" "Let's go!") Point event (12) Attention seeking Volunteer tries to get the attention of the dog and calls the dog by its name and/or the utterance of "Look!" and/or clicking the tongue ("tze tze" sound) Point event (12) High-pitched voice Volunteer talks to the dog with high pitched voice or with baby-talk expressions Point event (27) Praise Volunteer talks to the dog with a positive utterance (e.g., "Great!" "Well done!" "Good dog!") Point event (12,27) Negative verbal cue Volunteer talks to the dog with a negative utterance [e.g., "No!" "Bad dog!" "Don't …" "Stop chewing the lead" "Let the lead (it) go"] Point event ...
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Different people relate to dogs in different ways. We investigated differences between volunteers in their behavioural interactions with shelter dogs when they were walked on a leash. Cameras were used to record and quantify the behaviour of volunteers and a leash tension metre was used to measure pulling by both volunteers and shelter dogs. Effects of volunteers' age, body height, educational level, marital status, and experiences of living and working with dogs, and living with children, were examined. Older volunteers talked to the dogs more often during the walk than younger ones. Taller volunteers had reduced physical contact with dogs, and dogs pulled more frequently on the leash while walking with them. Volunteers with a postgraduate degree more frequently praised dogs and rewarded dogs with food and used more body language in the form of hand gestures and physical contact. Married and partnered volunteers more often praised dogs, while separated/divorced or widowed volunteers initiated more frequent physical contacts. Dogs pulled less when walking with volunteers who had experience of living with dogs, and these volunteers interacted with dogs using fewer verbal and body languages. Finally, those living with children more frequently communicated with dogs using body language (e.g., hand gestures and physical contact). We conclude that shelters should carefully consider volunteers' demographics when selecting them to walk dogs with various behavioural characteristics.
... Despite the comprehensive literature on human-dog interactions, limited research has focused on the influence of human personality (Kis et al., 2012). Human personality is widely accepted to be associated with our perceptions and behaviors (Tasa et al., 2010;Kis et al., 2012), including the way we interact with dogs (Wedl et al., 2010;Kis et al., 2012;Cimarelli et al., 2016Cimarelli et al., , 2017. ...
... Despite the comprehensive literature on human-dog interactions, limited research has focused on the influence of human personality (Kis et al., 2012). Human personality is widely accepted to be associated with our perceptions and behaviors (Tasa et al., 2010;Kis et al., 2012), including the way we interact with dogs (Wedl et al., 2010;Kis et al., 2012;Cimarelli et al., 2016Cimarelli et al., , 2017. The "similarity-attraction hypothesis, " which proposes that we share similar personality traits, physical attractiveness, and attitudes with our partners, has also been used to describe the owner-dog partnership . ...
... Despite the comprehensive literature on human-dog interactions, limited research has focused on the influence of human personality (Kis et al., 2012). Human personality is widely accepted to be associated with our perceptions and behaviors (Tasa et al., 2010;Kis et al., 2012), including the way we interact with dogs (Wedl et al., 2010;Kis et al., 2012;Cimarelli et al., 2016Cimarelli et al., , 2017. The "similarity-attraction hypothesis, " which proposes that we share similar personality traits, physical attractiveness, and attitudes with our partners, has also been used to describe the owner-dog partnership . ...
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Human personality influences the way people interact with dogs. This study investigated the associations between the personality of animal shelter volunteers and behavior during on-leash walks with shelter dogs. Video recording and a canine leash tension meter were used to monitor the on-leash walking. Personality was measured in five dimensions (neurotic, extroverted, open, agreeable and conscientious) with the NEO Five-Factor Inventory (NEO-FFI). Neurotic volunteers pulled the leash harder and tended to interact with dogs using more body language; dogs being walked by neurotic volunteers in turn displayed more lip-licking and body shaking and were more likely to be rated as well-behaved. Extroverted volunteers were associated with stronger maximal leash tension at both the human and dog ends of the leash, and they praised the dog more, often in a high pitched voice. These volunteers eliciting more tail-wagging and body shaking by the dog. Extroverted volunteers were also more tolerant of different dog behaviors. Volunteers with personalities characterized by “openness to experiences” were less likely to verbally attract the attention of dogs, praise dogs and talk to them in a high-pitched voice; however, dogs walked by these volunteers were more likely to pull on the leash, and engaged in more lip-licking but less sniffing. “Agreeable” volunteers liked to verbally attract the attention of the dogs and more commonly initiated hand gestures and physical contact, causing the dogs to pull less frequently; dogs in these dyads displayed more gazing and lip-licking behaviors. Conscientious volunteers were less likely to pull the leash and tended to have more physical contact with the dogs but did not favor verbal communication and did not use a high pitched voice.
... Ten randomly selected videos were recoded to check intra-observer reliability (Cohen's Kappa = 0.76). Canine behaviours, human verbal cues and human body language were coded using ethograms developed based on previous research [4,45,[54][55][56][57], as previously described [49] and modified during practice sessions (Tables 2-4). These tables are reproduced from Shih et al. [49] to help with the understanding of this paper. ...
... Point event [56] Attention seeking ...
... Point event [56] High-pitched voice Volunteer talks to the dog using a high-pitched voice or baby-talk expressions. Point event [4] Praise Volunteer talks to the dog with a positive utterance (e.g., "Great!" "Well done!" "Good dog!"). ...
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Inappropriate leash reactivity is one of the most common problems in shelter dogs, which negatively affects the health of dogs and reduces their adoptability. We explored 370 human-dog interactions, involving 74 volunteers and 111 dogs, in an animal shelter when volunteers walked shelter dogs on a leash, considering the effects of canine demographics and the results of the shelter’s canine behavioural assessments. The interaction was video recorded and coded using ethograms, and a leash tension meter was used to measure the pull strength of dogs and handlers. Results showed that dogs that were more relaxed during the shelter assessment (i.e., when socialising with humans or being left alone in a new environment) were less reactive on the leash, with lower tension and pulling frequency. Moreover, socialised and relaxed dogs displayed more positive body language, such as tail in a high position, gazing at the handler, and exploring the environment. When walking with these dogs, volunteers utilised fewer verbal cues and body language during the walk. In addition to the canine behaviour assessment, there were correlations between canine demographics and the behavioural interaction and humans’ perception. Finally, volunteers perceived the walk as less satisfactory when they needed to pull the leash harder during the walk. This research suggests that the RSPCA behavioural assessment may be useful in predicting the behaviour of shelter dogs when walked by volunteers.
... Previous work has examined how owner personality relates to dog aggression (Daye 2011;Podberscek and Serpell 1997), dog behavior problems (O'Farrell 1995(O'Farrell , 1997Dodman et al. 2018), dog separation anxiety (Konok et al. 2015), and dog-human relationships (Cavanaugh et al. 2008;Schöberl et al. 2012;Curb et al. 2013;Chopik and Weaver 2019;reviewed in Payne et al. 2015). Kis et al. (2012) explored the connection between owner personality and aspects of dog training and obedience. They found that owner personality (specifically neuroticism) was related to latency to follow commands. ...
... Surprisingly, the diligence required to consistently and successfully train a dog was not captured in the owner personality trait of conscientiousness. Also, neuroticism-a trait linked to command-following (Kis et al. 2012)-was not related to training success. Perhaps the brief Fig. 1 Effects of predictors on Canine Good Citizen training success. ...
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Teaching owners how to train their dogs is an important part of maintaining the health and safety of dogs and people. Yet we do not know what behavioral characteristics of dogs and their owners are relevant to dog training or if owner cognitive abilities play a role in training success. The aim of this study is to determine which characteristics of both dogs and owners predict success in completing the American Kennel Club Canine Good Citizen training program. Before the first session of a dog training course, owners completed surveys evaluating the behavior and cognition of their dog and themselves. Additionally, we collected the dogs’ initial training levels via behavioral tasks. We then examined what factors predicted whether the dogs passed the Canine Good Citizen test after the class ended. In terms of dog characteristics, we found that, while dog age, sex and neuter status did not predict success, owner-rated levels of disobedience did predict completion of the program. In terms of owner characteristics, owners who scored higher on cognitive measures were more likely to have their dogs complete the program. Finally, dog–owner characteristics such as the time spent training predicted success. Thus, characteristics of the dogs, owners, and how they interact seem to predict training success. These findings suggest that there are some owner, dog, and dog–owner characteristics that can facilitate or hinder dog training.
... Finally, owner's characteristics can also influence dog-owner interactions and should be considered in further studies of dogs' sleep quality. For instance, polymorphisms in the owners' OXTR gene are related to their dogs' attachment behavior in the SST test [41], whereas the owners' personality and interaction style influences their dogs' reaction to stressful situations (e.g., threatening approach) [61] or when fulfilling simple commands [62]. ...
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Affective neuroscience studies have demonstrated the impact of social interactions on sleep quality. In humans, trait-like social behaviors, such as attachment, are related to sleep brain activity patterns. Our aim was to investigate associations between companion dogs’ spontaneous brain activity during sleep (in the presence of the owner) and their relevant behavior in a task-free social context assessing their attachment towards the owner. In random order, each dog participated in a non-invasive sleep electroencephalogram (EEG) measurement and in the Strange Situation Test (SST) to assess their attachment behavior. We found that higher attachment scores were associated with more time spent in NREM sleep, lower NREM alpha power activity and lower NREM alpha–delta anticorrelation. Our results reveal that, when dogs sleep in a novel environment in the company of their owners, differences in their attachment are reflected in their sleep EEG characteristics. This could be best explained by the different degree that owners could be used as a safe haven in an unfamiliar environment and during the unusual procedure of the first EEG measurement. - Citation: Carreiro, C.; Reicher, V.; Kis, A.; Gácsi, M. Attachment towards the Owner Is Associated with Spontaneous Sleep EEG Parameters in Family Dogs. Animals 2022, 12, 895.
... Approach behaviors have been found to be related to traits such as Playful and Curious in another study of elephant personality (African elephants: Horback et al., 2013); which could explain the relationship between Walk and Curious in the present study. Contrary to our predictions, coded behavior traits and rated traits were not significantly correlated, so perhaps keepers consider their human-elephant interactions more than elephant-elephant interactions when assigning individual elephant personality (e.g., Kis et al., 2012;Mullan & Main, 2007; but see Bonaparte-Saller & Mench, 2018). It is also possible that keeper personality and experience influenced their rating of the elephants' personalities, as has been proposed in semicaptive elephant personality studies where mahouts score their elephant's personality (Seltmann et al., 2018). ...
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Animal personality has been shown to predict many behavioral responses across taxa, but the relationship between personality and performance on cognitive tasks remains unclear. To address this gap, we investigated whether personality predicted problem-solving performance and learning in captive Asian and African savanna elephants. We leveraged 3 novel problem-solving tasks to assess success rate, latency to touch the apparatus, exploratory diversity (the number of different behaviors exhibited toward the task), work time (the proportion of time working on the tasks), and latency to solve. To measure multiple different personality traits, such as boldness, activity, aggressiveness, curiosity, and sociability, across contexts, we carried out novel object presentations, behavioral coding through observations, and trait rating through surveys with zookeepers. We found evidence of personality through behavioral observations and surveys, but not through novel object testing. Aggressiveness and activity were important predictors of problem solving, but this was task-dependent, and the traits we measured did not significantly predict learning. Elephants solved 2 out of 3 tasks faster over time, but they did not vary their latency to touch, exploratory diversity, or work time. We discuss our results in terms of task difficulty and previous work on personality in elephants. Results from this study lay the foundation for future work connecting individual variation in personality to cognitive performance in elephants. In addition, for zoo-housed animals, individual differences research could inform enrichment and welfare decisions as well as conservation strategies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Play signals are known to function in the solicitation and maintenance of intraspecific play, but their role in interspecific play is relatively unstudied. We carried out two studies to examine interspecific signalling when humans play with domestic dogs, Canis familiaris. In the first, we recorded dog–owner play sessions on video to identify actions used by 21 dog owners to initiate play with their dogs. Thirty-five actions were each used by three or more owners. These included postures, vocalizations and physical contact with the dog. The actions varied greatly in their apparent success at instigating play which was, surprisingly, unrelated to the frequency with which they were used. We then did an experiment to determine the effect of composites of commonly used signals upon the behaviour of 20 Labrador retrievers. The performance of both ‘Bow’ and ‘Lunge’ by a human altered the subsequent behaviour of the dogs. Both signals caused increases in play, and Lunge produced significant increases in play bout frequency and mean bout duration. The efficiency of both these postural signals was enhanced when they were accompanied by play vocalizations. Thus, specific actions used by humans do communicate a playful context to dogs and can be described as interspecific play signals.
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In two studies, we have investigated the co-operative behaviour between dogs and their owners. We supposed that co-operative behaviour is an inherited trait in dogs, and is a major contributing factor in the development of successful guide dog performance. According to our view, leading a blind person involves complex behaviour where success depends on the ability of the participants to synchronise their actions. In Study I, we observed both British and Hungarian blind owners taking a half-hour walk in their neighbourhood. In Study II, both guide dogs with their blind and pet dogs with their blind-folded owners had to master an obstacle course. Measuring the frequency of initiations of various actions during leading their owners, dogs did not keep the role of the initiator to themselves. However, both dogs and humans were found to initiate more often in some types of actions, for example, guide dogs initialised avoidance or stepping up more often than their owners. Further, the role of the initiator was kept only for short durations, longer sequences of initialising were rare.Despite many differences among groups studied, we observed some qualitative similarities in the co-operative behaviour of dogs. We assume that during domestication, dogs have been selected for the ability to change to-and-fro the role of the initiator that seems to be fundamental in this type of co-operation. In the case of leading the blind, information should not only be provided but also accepted by both parties in the course of the joint actions, therefore, the leadership (the role of the initiator) may vary form one action to the next.
Personality matching in owner-dog dyads is relatively understudied. The current research takes a cross-species approach to investigate the relationship between the personality traits of the owners and of their dogs and the association between ''relationship satisfaction'' and dog/owner personality traits. 120 adult owners with one dog were asked to fill out two personality questionnaires and to rate their relationship with their dog on a 10-point scale. We compared the owners' and the dogs' personality assessment in extraver-sion, neuroticism, agreeableness and openness traits using Spearman rank order correlations. Univariate GLM was used to examine the effects of the human and dog personality traits on the relationship satisfaction. Significant positive correlations were found between the owners' and dogs' personality assessment in neuroticism and extraversion. The relationship satisfaction reported by the owner was related only to the dogs' personality traits (neuroticism, extraver-sion), none of the human traits were associated with it. However, the assessment of the dog's personality may be biased by the owners' tendency to project their own characteristics onto their dogs. To address this issue, we asked 26 owners with more than one dog to assess both their first and second dog. Owners assessed their first dog similarly to themselves in neuroticism, but the correlation in extraver-sion was not significant. In the case of the second dog, the correlation in neuroticism was negative, while in extraver-sion it was positive. However, the first and second dog did not differ from each other in either personality traits. These findings support that owners do not project indiscriminately their own characteristics onto their dogs. Our results show a convergence between the neuroticism and extraversion traits of dogs and their owners, which might be related to the number of dogs in the household and the order of acquiring the dogs.