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Humans rely on the same rules to assess emotional valence and intensity in conspecific and dog
vocalizations
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Tamás Faragó1*, Attila Andics1, Viktor Devecseri1, Anna Kis2,3, Márta Gácsi1, Ádám Miklósi1,2
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*Correspondence: mustela.nivalis@gmail.com
MTA-ELTE Comparative Ethology, Budapest, Hungary. H-1117 Pázmány Péter sétány 1/C.
Department of Ethology, Eötvös University, Budapest, Hungary. H-1117 Pázmány Péter sétány 1/C.
Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary. H-1132
Victor Hugo utca 18-22.
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1 Summary
Humans excel at assessing conspecific emotional valence and intensity based solely on nonverbal
vocal bursts that are also common in other mammals. It is not known, however, whether human
listeners rely on similar acoustic cues to assess emotional content in conspecific and heterospecific
vocalizations, and which acoustical parameters affect their performance.
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Here, for the first time, we directly compared the emotional valence and intensity perception of dog
and human nonverbal vocalizations. We revealed similar relationships between acoustic features and
emotional valence and intensity ratings of human and dog vocalizations: those with shorter call lengths
were rated as more positive, while those with a higher pitch were rated as more intense.
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Our findings demonstrate that humans rate conspecific emotional vocalizations along basic acoustic
rules, and that they apply similar rules when processing dog vocal expressions. This suggests that
humans might utilize similar mental mechanisms for recognizing human and heterospecific vocal
emotions.
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Keywords: dog; human; vocal communication; emotion valence assessment; emotion intensity
assessment; nonverbal emotion expressions
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2 Introduction
Emotions are an organism’s specialized mental states, shaped by natural selection, enabling them to
increase fitness in certain contexts by facilitating adaptive physiological, cognitive and behavioural
responses [1]. Non-linguistic vocal emotional expressions are ancient, evolutionarily conservative,
easily recognized by humans [2], and less affected by cultural differences than prosody or linguistic
emotional expressions [3]. Most emotional vocalizations consist of calls that are acoustically highly
similar in both humans and other species [4]. These calls, as the smallest meaningful units, are the
building blocks of vocal emotion expressions and their acoustic properties affect how listeners
perceive their emotional content [5].
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According to the ‘pre-human origin’ hypothesis of affective prosody, the acoustic cues of emotions in
human vocalizations are innate and have strong evolutionary roots [6]. Furthermore, according to the
Source-Filter Framework, the basic mechanisms of sound production are the same among human and
nonhuman animals [7], suggesting that similar vocal parameters may carry information for the
listeners about the caller’s inner state [8]. We can therefore hypothesize that similar basic rules support
vocal emotion recognition both within and across species. However, we have little information about
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how the wide variety of possible emotional states are encoded and perceived in vocalizations, and
whether humans and non-human animals use these parameters or follow other rules when processing
emotional sounds.
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Dogs, due to their special status in the human society [9] and their numerous vocalization types used
in various social contexts [10], can provide an excellent insight into this question. Recent studies
showed that the acoustics of dog barks affects humans’ inner state assessment following the MS rules
[11]: low pitched barks with short inter-bark intervals were rated as aggressive, while high pitched
ones with long intervals were considered playful and happy. However, it is not clear yet whether the
same principle stands true across the diverse vocal repertoire. More importantly, based on the
analogous vocal production mechanisms and the pristine nature of the human nonverbal vocal
emotional expressions, we can predict that humans use similar features of human and dog
vocalizations to assess the signaller’s inner state. Thus, our aim was to compare which basic acoustical
properties of dog and human vocalizations affect how human listeners assess their emotional content.
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There are two main approaches to study emotions: the framework of discrete emotions is rooted in
studies of human facial expressions and claims to focus on “pure” emotions. In contrast, dimensional
models aim to account for gradedness found in studies of subjective experiences of emotions, and
suggest that inner states can be effectively modelled as coordinates of a two or three dimensional
space [12]. Following Mendl’s suggestion, we adapted Russell’s widely used dimensional model [13]
and asked listeners to rate human and dog vocalizations along two parameters: 1) emotional valence,
ranging from negative to positive, and 2) emotional intensity (we use this term as a synonym of
emotional arousal as in [6]), ranging from non-intense to intense. To explore how specific acoustic
cues affect the ratings of human and dog vocalizations, we measured four basic parameters for each
vocal stimulus. Fundamental frequency (F0) and tonality (harmonic-to-noise ratio: HNR) are used as
inner state indicators in the MS rules, and, as source related parameters, they are potentially affected
by arousal and emotional quality [7]. In addition, spectral centre-of-gravity (CG) of vocalizations had
been found to affect the perception of valence and arousal in human nonverbal vocalizations [5].
Finally, the average call length (CL) within a sound sample is also a commonly measured temporal
parameter linked to the emotional state of the signaller [6,8].
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3 Methods
Our subjects were Hungarian volunteers, recruited via the internet and through personal requesting (6
males, 33 females, age: 31±9 years, Table S3). We compiled a pool of 100-100 nonverbal
vocalizations of dogs and humans from diverse social contexts and various sound types (for details see
Supplementary Methods). Acoustical measurements were carried out with a semiautomatic Praat
script. First, each basic vocal unit within a sound sample was marked, to be considered later on as an
individual call (Figure S1, see a similar approach in [4]). We measured CL, F0, HNR, and CG in each
call. Then, these call-by-call measurements were averaged within each sound sample to characterize
each sample by one value of the given parameter (standard deviation across all calls was 2-5 times
greater than the average within-sample standard deviation, for all acoustic variables).
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A novel online based survey (http://www.inflab.bme.hu/~viktor/sr_demo/) was developed to assess how
humans perceive the emotional content of vocalizations. Instead of using independent basic emotion
scales, we applied a slightly modified version of Russell’s two-dimensional model (Figure1). Subjects
rated the emotional valence and intensity of the sounds by clicking on one point of a coordinate
system. The system registered the two coordinates (valence: -50–50; intensity: 0-100), and the reaction
time. After three practice trials, all 200 stimuli were presented randomly. Every sample was played
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once for each subject, except for 5-5 selected and randomly repeated dog and human samples used to
test the reliability of subjects’ responses (See Supplementary material). We also added two breaks,
unrestricted in length, after the 70th and the 141st sound. We analysed the data of those (N=39) subjects
who completed the survey for all sound samples.
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To reveal the effects of acoustic parameters on the responses, multivariate linear regressions were
applied. For this, we averaged the valence and intensity ratings within each sample. We used a
backward elimination method to find the parameters that affected the ratings most (one dog sample
was excluded due to its extreme high fundamental frequency - 3500Hz).
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4 Results
The regression models showed significant relationships between emotional ratings and acoustic
measures for both dog and human vocalizations. We found that valence ratings were affected by CL in
both dog and human samples: the shorter the calls were within a sound sample, the more positively the
sample was rated. For human sounds, lower CG values corresponded with more positive valence
scores. The intensity scale was also affected by the measured acoustical parameters in both human and
dog sounds. Partial regressions showed that the intensity was influenced by F0: higher pitched samples
were rated as more intense in both species’ vocalizations. We also found species-specific effects where
the intensity ratings of dog samples were affected by the change of the other three acoustical
parameters: longer and more tonal dog samples were rated as less intense, while higher CG was related
to higher intensity ratings (Figure2, for statistical details see Table1).
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5 Discussion
This study is the first to directly compare how humans perceive human and dog emotional
vocalizations. We show that humans use similar acoustical parameters to attribute emotional valence
and intensity to both human and dog vocalizations.
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Our results support the pre-human origin hypothesis of affective prosody [6], and are indicative of
similar mechanisms underlying the processing of human and dog vocal emotion expressions.
Evolutionary ancient systems could possibly be used for processing the emotional load of
nonlinguistic human and non-human vocalizations. Alternatively, humans may judge the emotional
states of non-human animal sounds on the basis of perceived acoustic similarity to their own
vocalisations.
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Our results are in agreement with previous studies aiming to assess the acoustic rules underlying the
processing of different vocalizations. However, we also reveal novel and previously unexplored
relationships. Pongrácz et al. found that, in case of dog barks [11], deeper pitch and fast pulsing can be
linked to higher aggression, while low pulsing and higher pitch to positive valence, and higher tonality
to higher despair ratings. In contrast, our results show that, with regards to dog vocalizations, long,
high pitched and tonal sounds can be linked to fearful inner states (high intensity, negative valence),
long, low pitched, noisy sounds to aggressiveness (lower intensity, still negative valence), and short,
pulsing sounds independent of their pitch and tonality are connected to positive inner states. Since
barks are highly specialized vocalizations of dogs formed by domestication [14], no general rule can
be drawn based on their acoustical structure. In our study, high diversity of calls showed clear parallels
with assessing human emotional valence and intensity. Sauter et al [5] reported similar effects of
fundamental frequency in intensity and spectral-centre-of-gravity in valence ratings, while, in contrast
with our results, they found call length affecting the intensity ratings negatively and spectral-centre-of-
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gravity positively. These differences may be due to the different composition of the presented stimuli.
While Sauter’s recordings of 10 acted emotions originated from 4 adult vocalizers, our sample
(although still cannot be fully representative) covered a wider range of call types and vocalizers
resulting in higher acoustic variance and revealing different connections. Besides the basic parameters
investigated here, several others may play a role in valence perception (for a review see: [8]).While
our within-sample averaging approach was insensitive to the within-sample dynamics of acoustic
parameters across consecutive calls, such dynamic changes may also convey relevant information
about the inner state of the signaller. More studies are needed to determine whether acoustic
similarities between human and dog vocalizations also reflect functional similarity in their emotional
states.
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To conclude, our results provide the first evidence of the use of the same basic acoustic rules in
humans for the assessment of emotional valence and intensity in both human and dog vocalizations.
Further comparative studies using vocalizations from a wide variety of species should reveal the
existence of a common mammalian basis for emotion communication, as suggested by our results.
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6 Acknowledgements
The study was supported by The Hungarian Academy of Sciences (F01/031), the Hungarian Scientific
Research Found (OTKA-K100695) and an ESF Research Networking Programme "CompCog": The
Evolution of Social Cognition (06-RNP-020). We are thankful to our reviewers for their insightful
comments and Zita Polgar for correcting our English.
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1
Nesse, R. M. 1990 Evolutionary explanations of emotions. Hum. Nat. 1, 261–289.
(doi:10.1007/BF02733986)
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2
Simon-Thomas, E. R., Keltner, D. J., Sauter, D. A., Sinicropi-Yao, L. & Abramson, A. 2009
The voice conveys specific emotions: evidence from vocal burst displays. Emotion 9, 838–46.
(doi:10.1037/a0017810)
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Sauter, D. A., Eisner, F., Ekman, P. & Scott, S. K. 2010 Cross-cultural recognition of basic
emotions through nonverbal emotional vocalizations. Proc. Natl. Acad. Sci. U. S. A. 107, 2408–
12. (doi:10.1073/pnas.0908239106)
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Bachorowski, J.-A., Smoski, M. J. & Owren, M. J. 2001 The acoustic features of human
laughter. J. Acoust. Soc. Am. 110, 1581. (doi:10.1121/1.1391244)
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Sauter, D. A., Eisner, F., Calder, A. J. & Scott, S. K. 2010 Perceptual cues in nonverbal vocal
expressions of emotion. Q. J. Exp. Psychol. 63, 2251–72. (doi:10.1080/17470211003721642)
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Zimmermann, E., Leliveld, L. & Schehka, S. 2013 Toward the evolutionary roots of affective
prosody in human acoustic communication: A comparative approach to mammalian voices. In
Evolution of Emotional Communication: from Sounds in Nonhuman Mammals to Speech and
Music in Man. (eds E. Altenmüller S. Schmidt & E. Zimmermann), pp. 116–132. Oxford, UK:
Oxford University Press.
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Taylor, A. M. & Reby, D. 2010 The contribution of source-filter theory to mammal vocal
communication research. J. Zool. 280, 221–236. (doi:10.1111/j.1469-7998.2009.00661.x)
References
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Briefer, E. F. 2012 Vocal expression of emotions in mammals: mechanisms of production and
evidence. J. Zool. 288, 1–20. (doi:10.1111/j.1469-7998.2012.00920.x)
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Topál, J., Miklósi, Á., Gácsi, M., Dóka, A., Pongrácz, P., Kubinyi, E., Virányi, Z. & Csányi, V.
2009 The dog as a model for understanding human social behavior. Adv. Study Behav. 39, 71–
116. (doi:10.1016/S0065-3454(09)39003-8)
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Cohen, J. A. & Fox, M. W. 1976 Vocalizations in wild canids and possible effects of
domestication. Behav. Processes 1, 77–92. (doi:10.1016/0376-6357(76)90008-5)
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Pongrácz, P., Molnár, C. & Miklósi, Á. 2006 Acoustic parameters of dog barks carry emotional
information for humans. Appl. Anim. Behav. Sci. 100, 228–240.
(doi:10.1016/j.applanim.2005.12.004)
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Mendl, M. T., Burman, O. H. P. & Paul, E. S. 2010 An integrative and functional framework
for the study of animal emotion and mood. Proc. R. Soc. B Biol. Sci. , 2895–2904.
(doi:10.1098/rspb.2010.0303)
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Russell, J. A. 1980 A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178.
(doi:10.1037/h0077714)
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Pongrácz, P., Molnár, C. & Miklósi, Á. 2010 Barking in family dogs: an ethological approach.
Vet. J. 183, 141–7. (doi:10.1016/j.tvjl.2008.12.010)
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8 Figure captions
Figure 1: Two-dimensional response plane. X and Y axes represent emotional valence and emotional
intensity, respectively. The system projects the cursor’s position to both axes (opaque area) to visually
help the subjects to rate stimuli along both dimensions at the same time. The white X shows where the
subject clicked to rate the actual sound.
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Figure 2: The linear relationships between acoustic parameters and emotional scales. Full circles
represent dog vocalizations, empty circles represent human vocalizations. The asterisks show
significant relationship between the measures (*<0.05; **<0.01; ***<0.001)
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9 Tables
Table 1: The results of the multivariate linear regressions. The table shows only the data of models
obtained by backward elimination (Model lines), and the partial regressions of the remaining
parameters. The grey cells show the eliminated parameters. Std β: Standardized Beta value, CL: call
length, F0: fundamental frequency, HNR: Harmonic-to-Noise ratio, CG: spectral centre of gravity.
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Acoustic parameters
Valence
R
2
Intensity
F
p
R
0.292
37.564
0.00
Std. β
t
p
-0.541
-6.129
2
F
p
0.436
17.01
0.00
Std. β
t
p
Partial
regressions
Dog
Model
CL
0.00 -0.215
-2.214
0.03
F0
0.320
2.423
0.02
HNR
-0.453
-4.345
0.00
CG
0.235
2.088
0.04
Partial
regressions
Human
Model
CL
R2
F
p
R2
F
p
0.119
6.572
0.00
0.179
21.387
0.00
Std. β
t
p
Std. β
t
p
-0.188
-1.969
0.05
0.423
4.625
0.00
F0
HNR
CG
-0.282
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10 Short title
Vocal emotion assessment by humans
-2.951
0.00
1
Supplementary methods
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Samples
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Samples were 2 s long slices cut from original recordings, containing an average of 3.4 calls (ranging
from one call to a series of twelve calls). The samples’ time structure and the call series contained
were left in their original natural state and were normalized to -26dB RMS, downsampled to 22.5kHz,
and saved as 16bit PCM wav files.
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We aimed to cover the vocal repertoire of the dog as comprehensively as possible; therefore we picked
vocalizations collected from various contexts and representing various vocalization types from the dog
sound database of the Family Dog Project (Table S1).
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The human nonverbal vocalizations were collected from available databases used in earlier studies to
assess emotional expressions [1–3]. These contained both natural and acted expressions, respiratory
sounds (e.g. cough, retch), and nonsense babbling (Table S1).
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Acoustical measures
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During sound analysis, we first applied Praat’s built-in automatic utterance finder function, then we
checked whether the algorithm determined the call borders properly, and modified if necessary. These
borders were used to measure the call lengths. Due to the diverse nature of the sounds, we inspected
the spectrogram of every sample and set a possible pitch floor and ceiling for each sample to optimize
the fundamental frequency searching algorithm (autocorrelation method). Finally, we applied Praat’s
cross-correlation based harmonicity function to extract the tonality of each call.
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Questionnaire
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The webpage of the survey can be found following this link:
http://www.inflab.bme.hu/~viktor/soundrating/index.html
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(Please note that the survey at the time of the paper’s submission is still active and used for data
collection, thus the responses might be used in a new study)
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Data analysis
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We did not find any differences in either the valence, or the intensity scores between the first and
second presentations of the ten repeated samples per subject (valence: t(38)=0.694, p=0.492; intensity:
t(38)=-0.247, p=0.806), therefore, all second presentations were excluded from the analysis. To avoid
the distorting effect of responses with unnaturally long reaction times, trials with reaction times from
the upper 5% quartile (>11.3s) were filtered out.
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To compare the variances in subjects’ responses to dog vs. human vocalizations, we averaged and
calculated the standard deviation of the valence and intensity ratings and the reaction times both per
sound sample (used for Mann-Whitney U tests) and per subject (for Wilcoxon signed-rank tests).
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In order to test the effect of the subjects’ dog ownership, ratings within dog and human sounds were
averaged for each individual and the range of responses were given by their standard deviation. For
this we pooled together the subjects who owned a dog at the time of responding with those who used
to have a dog before and considered themselves as owners (N=23).Non-owners were the subjects who
had never owned a dog or only their family had one (N=16). Mann-Whitney U tests were used to
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compare the responses of dog owners and non-dog owners and also to test for possible gender
differences.
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To validate listeners’ valence ratings of dog vocalizations, we grouped the vocalizations with
unambiguously valenced contexts (15 out of 16 independent raters) into two groups: threatening,
guarding, pup before feeding, bored and separation were classified as negative (N=32) vs. greeting,
petting, asking for toy and play as positive (N=33) contexts (see Supplementary Table4). We
compared the valence ratings between the two groups with Mann-Whitney U test.
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As barks are the most prominent vocalizations of dogs, and 27% of our sample was this type of
vocalization, we wanted to test whether the dominance of barks affect our results. For this we ran the
same linear regression analysis as in the main study after excluding barks from the sample.
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Supplementary results
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The emotional ratings of the sound samples also reflected some species-specific differences, (Table
S1). Subjects responded slower to dog vocalizations (U= 1865; p<0.001) and found them more intense
(U=2434, p<0.001), while there was no difference between the human and dog vocalizations’ valence
scores (U=5415, p=0.31). The within sample standard deviation of the dog vocalizations’ valence
ratings was significantly higher (U=2474; p<0.001), whereas the variance of their intensity ratings did
not differ (U=5259.5; p=0.526). In contrast, human valence and intensity ratings were both
significantly more variable than the ratings of dog stimuli (N=39; valence: W=67; p<0.001; intensity:
W=121; p<0.001).
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No gender difference was found (averages: human - valence: U=106, p=0.805; intensity: U=96,
p=0.924; dog - valence: U= 96, p= 0.924; intensity: U=87; p=0.662; range: human - valence: U=67,
p=0.227; intensity: U=115, p=0.556; dog - valence: U= 54, p= 0.083; intensity: U=63; p=0.171).
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Dog ownership had no influence on the average or range of the ratings (averages: human - valence:
U=203, p=0.601; intensity: U=139, p=0.207; dog - valence: U= 201, p= 0.641; intensity: U=217;
p=0.358; range: human - valence: U=149, p=0.329; intensity: U=144.5, p=0.263; dog - valence: U=
171, p= 0.724; intensity: U=176; p=0.832), or on the reaction times (human: U=150; p=0.343; dog:
U=168; p=0.662).
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The comparison between the dog sounds originating from social contexts with assumed positive and
negative valence showed significant difference in their valence ratings: dog vocalizations recorded in
negative contexts were also rated more negative by our subjects (U=789; p=0.001).
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Finally our second regression analysis showed that the same acoustical parameters affected the valence
(R2=0.291; F=27.718; p<0.001; partial regression: CL: Std. β=-0.55; t=5.265; p<0.001) and intensity
(R2=0.432; F=27.718; p<0.001; partial regressions: CL: Std. β=-0.343; t=3.089; p=0.003; F0: Std.
β=0.457; t=3.452; p=0.001; HNR: Std. β=-0.46; t=3.64; p=0.001) ratings after removing dog barks
from the sample, with the exception of spectral-centre-of-gravity.
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Supplementary discussion
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Note that only 15% of the participants were male. This bias most probably arose due to females’
higher willingness to participate in studies about pets, as also noted by Gosling and Bonnenburg [4].
The lack of control over subject selection is a possible unavoidable drawback of the open, online
questionnaire method applied here. However, despite the possible gender differences in emotion
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processing (for review see e.g. [5]) we found no gender effect here, suggesting that our results are not
compromised by the gender-biased sample.
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Subjects gave significantly more negative ratings to dog vocalizations recorded from contexts such as
guarding food from another dog, or strange human standing at the fence of the household, than to dog
vocalizations recorded in playful or greeting situations. This finding confirms earlier reports that
humans tend to attribute adequate inner states to heterospecific vocalizers [6,7].
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The fact that the relationship between the call length and fundamental frequency of dog vocalizations
and their valence and intensity ratings were the same after excluding dog barks suggests that the
pattern we found is not just simply caused by the dominance of barks in our sample , but it is generally
true across multiple vocalization types.
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The systematic differences in emotional valence and intensity ratings between human and dog stimuli
add to the body of evidence showing that the human auditory processing is tuned for conspecific
voices [3]. Human valence and human intensity scores were more variable across stimuli, but human
valence scores were also more consistent within stimulus, across subjects. These, together with the
finding that subjects rated human vocalizations with a faster latency, suggest that humans could assess
the conspecific vocal emotional load more easily than that of dogs. The finding that there is no strong
effect of dog ownership is also in line with earlier results based on dog barks. Humans recognize dog
barks with similar success independently of their prior knowledge about dogs, and the emotional
ratings of dog owners and non-dog owners do not differ either [6,8]. This provides further support to
the innate nature of emotional valence and intensity perception and to the ‘pre-human origin’
hypothesis [9].
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Supplementary references
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1
Belin, P., Fillion-Bilodeau, S. & Gosselin, F. 2008 The Montreal Affective Voices: A validated
set of nonverbal affect bursts for research on auditory affective processing. Behav. Res.
Methods 40, 531–539. (doi:10.3758/BRM.40.2.531)
104
105
106
2
Bradley, M. M. & Lang, P. J. 2007 The International Affective Digitized Sounds (2nd Edition
IADS-2): Affective ratings of sounds and instruction manual. Univ. Florida, Gainesville, FL,
Tech. ….
107
108
3
Belin, P., Zatorre, R. J., Lafaille, P., Ahad, P. & Pike, B. 2000 Voice-selective areas in human
auditory cortex. Nature 403, 309–12. (doi:10.1038/35002078)
109
110
111
4
Gosling, S. & Bonnenburg, A. 1998 An integrative approach to personality research in
anthrozoology: ratings of six species of pets and their owners. Anthrozoös A Multidiscip. J.
Interact. People Anim. 11, 148–156.
112
113
5
Cahill, L. 2006 Why sex matters for neuroscience. Nat. Rev. Neurosci. 7, 477–84.
(doi:10.1038/nrn1909)
114
115
116
6
Pongrácz, P., Molnár, C., Miklósi, Á. & Csányi, V. 2005 Human listeners are able to classify
dog (Canis familiaris) barks recorded in different situations. J. Comp. Psychol. 119, 136–44.
(doi:10.1037/0735-7036.119.2.136)
117
118
119
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Tallet, C., Špinka, M., Maruscáková, I. & Simecek, P. 2010 Human perception of vocalizations
of domestic piglets and modulation by experience with domestic pigs (Sus scrofa). J. Comp.
Psychol. 124, 81–91. (doi:10.1037/a0017354)
120
121
122
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Molnár, C., Pongrácz, P. & Miklósi, Á. 2009 Seeing with ears: Sightless humans’ perception of
dog bark provides a test for structural rules in vocal communication. Q. J. Exp. Psychol. , 1–10.
(doi:10.1080/17470210903168243)
123
124
125
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127
9
Zimmermann, E., Leliveld, L. & Schehka, S. 2013 Toward the evolutionary roots of affective
prosody in human acoustic communication: A comparative approach to mammalian voices. In
Evolution of Emotional Communication: from Sounds in Nonhuman Mammals to Speech and
Music in Man. (eds E. Altenmüller S. Schmidt & E. Zimmermann), pp. 116–132. Oxford, UK:
Oxford University Press.
128
Species
Dog
Dog
Dog
Dog
Dog
Dog
Dog
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Call type
Bark
Growl
Grunt
Moan
Pant
Whine
Yelp
Cough
Cry
Erotic moan
General
Human moan
Laugh
Retch
Scream
Shout
Sigh
Yawn
number of samples
context
asking for toy
begging for food
before walk
bored
before snow shoveling
foodguarding
greeting
neutral
petting
play
pup before feeding
separation
dynamic threatening
asked to speak
stranger at fence
threatening stranger
assumed valence
+
+
+
+
+
+
+
-
aggreement
27
24
11
17
3
14
4
10
16
8
21
9
16
3
4
6
5
2
94%
63%
88%
94%
75%
94%
100%
75%
100%
100%
69%
94%
100%
63%
100%
100%
sound ID
dog_s001
dog_s002
dog_s003
dog_s004
dog_s005
dog_s006
dog_s007
dog_s008
dog_s009
dog_s010
dog_s011
dog_s012
dog_s013
dog_s014
dog_s015
dog_s016
dog_s017
dog_s018
dog_s019
dog_s020
dog_s021
dog_s022
dog_s023
dog_s024
dog_s025
dog_s026
dog_s027
dog_s028
dog_s029
dog_s030
dog_s031
dog_s032
dog_s033
dog_s034
dog_s035
dog_s036
dog_s037
dog_s038
dog_s039
dog_s040
dog_s041
dog_s042
dog_s043
dog_s044
dog_s045
dog_s046
dog_s047
dog_s048
dog_s049
dog_s050
dog_s051
dog_s052
dog_s053
dog_s054
dog_s055
dog_s056
dog_s057
dog_s058
dog_s059
dog_s060
dog_s061
dog_s062
dog_s063
dog_s064
dog_s065
dog_s066
dog_s067
dog_s068
dog_s069
dog_s070
dog_s071
dog_s072
dog_s073
dog_s074
dog_s075
dog_s076
dog_s077
dog_s078
dog_s079
dog_s080
dog_s081
dog_s082
dog_s083
dog_s084
dog_s085
dog_s086
dog_s087
dog_s088
dog_s089
dog_s090
dog_s091
dog_s092
dog_s093
dog_s094
dog_s095
dog_s096
dog_s097
dog_s098
dog_s099
dog_s100
Call type
Bark
Growl
Growl
Bark
Growl
Bark
Bark
Bark
Grunt
Moan
Grunt
Growl
Bark
Grunt
Moan
Bark
Growl
Bark
Growl
Whine
Bark
Moan
Bark
Bark
Moan
Whine
Grunt
Yelp
Growl
Bark
Moan
Growl
Growl
Grunt
Pant
Growl
Yelp
Grunt
Whine
Grunt
Yelp
Bark
Pant
Grunt
Bark
Whine
Bark
Whine
Growl
Growl
Growl
Whine
Whine
Bark
Growl
Bark
Bark
Growl
Bark
Moan
Moan
Whine
Moan
Bark
Growl
Moan
Growl
Whine
Grunt
Bark
Moan
Moan
Growl
Moan
Whine
Whine
Bark
Bark
Whine
Growl
Pant
Bark
Whine
Growl
Moan
Growl
Grunt
Grunt
Whine
Bark
Bark
Moan
Growl
Moan
Bark
Moan
Growl
Moan
Yelp
Growl
Call number
3
2
1
4
3
2
5
4
4
4
6
3
4
6
1
4
4
2
1
2
2
2
4
3
1
2
9
6
1
3
4
1
1
8
4
3
2
5
2
2
6
4
11
7
4
8
3
1
5
1
1
6
3
4
1
5
4
1
7
3
3
1
1
6
1
4
2
4
5
3
2
1
1
2
4
4
4
3
5
4
6
3
5
5
6
5
3
3
4
3
4
3
5
2
6
1
3
3
3
1
Call length (s)
Average Standard Deviation
0,30
0,03
0,95
0,63
2,00
0,18
0,02
0,37
0,09
0,16
0,00
0,24
0,08
0,23
0,04
0,50
0,25
0,38
0,23
0,29
0,13
0,63
0,34
0,22
0,04
0,26
0,09
2,00
0,18
0,01
0,28
0,06
0,23
0,02
2,00
0,78
0,35
0,32
0,02
0,73
0,04
0,22
0,03
0,17
0,01
1,43
0,86
0,02
0,22
0,05
0,22
0,03
1,06
0,29
0,05
0,50
0,17
2,00
2,00
0,23
0,05
0,35
0,12
0,62
0,09
0,84
0,05
0,40
0,07
0,87
0,35
0,49
0,04
0,28
0,05
0,24
0,03
0,18
0,04
0,29
0,05
0,20
0,03
0,25
0,05
0,23
0,02
1,89
0,40
0,14
1,58
1,69
0,17
0,02
0,52
0,18
0,16
0,00
1,18
0,24
0,02
0,29
0,07
1,90
0,25
0,07
0,66
0,25
0,57
0,13
1,78
2,00
0,17
0,03
1,96
0,41
0,17
0,72
0,01
0,41
0,19
0,34
0,05
0,23
0,03
0,74
0,01
2,00
0,72
0,97
0,48
0,42
0,14
0,23
0,08
0,29
0,14
0,22
0,02
0,30
0,15
0,35
0,09
0,31
0,06
0,19
0,01
0,35
0,05
0,23
0,11
0,19
0,12
0,37
0,23
0,67
0,18
0,57
0,15
0,50
0,03
0,23
0,02
0,21
0,01
0,63
0,04
0,36
0,07
0,63
0,01
0,25
0,03
2,00
0,67
0,10
0,63
0,13
0,29
0,05
2,00
Fundamental frequency (Hz)
Harmonic-to-Noise ratio
Average
Standard Deviation Average Standard Deviation
374,33
11,45
5,27
0,96
72,07
3,81
8,18
3,93
86,35
8,26
550,04
16,19
16,19
1,69
undefined
0,01
1,09
500,74
5,40
12,07
1,01
426,73
125,79
6,11
1,22
613,17
22,20
6,06
2,35
86,16
1,11
2,58
2,25
419,34
46,72
12,71
3,76
undefined
-1,74
0,68
122,02
5,37
0,31
1,02
503,02
135,78
6,38
0,87
109,68
1,08
0,39
1,16
217,06
5,39
436,62
35,76
5,78
0,48
197,15
19,95
4,09
0,79
443,02
3,69
20,84
2,89
77,79
14,12
506,14
24,50
11,65
5,54
486,04
3,85
8,36
0,50
152,75
24,96
3,54
2,16
393,92
61,75
5,07
2,17
484,19
7,06
10,36
2,90
99,84
0,00
4,54
0,00
365,91
25,00
15,21
1,67
165,61
10,39
0,27
0,59
796,44
139,06
17,17
4,08
149,96
6,92
299,75
19,20
10,99
1,33
268,41
16,43
7,88
2,19
159,34
5,44
152,97
3,01
undefined
0,57
2,03
undefined
-2,03
0,57
246,93
4,23
3,42
0,83
473,25
21,85
16,00
1,44
75,04
7,44
0,21
0,89
840,73
19,04
23,72
5,48
84,58
1,62
4,19
0,53
720,89
166,31
11,69
2,44
545,57
45,95
9,33
0,84
109,30
0,00
-0,78
0,39
114,60
0,00
0,80
1,35
380,77
6,23
12,32
4,06
975,23
549,08
7,88
2,86
376,09
44,71
3,58
0,69
590,60
18,74
142,82
9,87
6,87
3,28
65,49
1,32
113,21
1,75
513,49
95,01
19,08
3,46
1428,18
86,71
27,22
3,68
581,24
14,17
21,59
1,14
90,79
2,03
351,14
82,97
6,34
1,82
389,44
70,22
6,95
1,96
97,37
9,84
405,20
43,28
6,32
2,46
346,38
10,64
2,29
0,21
582,01
110,31
6,07
1,66
406,14
24,85
134,53
12,28
549,02
77,21
10,25
3,13
51,12
3,01
137,43
53,08
4,38
1,51
284,29
19,08
6,19
1,50
556,01
67,90
10,69
3,08
178,71
24,86
4,03
1,85
495,03
18,07
13,91
3,09
346,74
25,31
9,65
2,53
230,69
23,40
142,70
6,99
280,49
0,00
4,21
1,46
392,44
34,57
12,02
4,76
1607,28
2009,48
7,09
4,09
731,53
73,73
10,04
1,48
452,55
11,01
6,56
0,96
1074,59
144,43
15,09
3,69
215,90
29,96
6,61
3,68
184,47
34,99
0,46
0,44
552,73
3,85
12,10
1,52
694,98
205,43
8,97
1,79
327,81
112,57
1,04
0,58
679,53
248,40
13,81
4,83
82,64
8,05
2,42
1,77
undefined
-1,14
0,89
160,11
75,18
-0,90
0,99
3591,86
25,44
2,71
1,71
545,11
2,75
9,06
1,06
580,69
53,25
8,09
2,08
429,99
193,20
4,23
1,96
186,61
16,06
1,63
0,51
349,10
9,03
5,57
1,54
674,67
50,17
7,53
1,64
399,37
17,97
271,04
5,27
5,09
1,04
285,62
23,16
5,06
1,34
671,35
36,32
10,85
4,79
112,67
0,07
Spectral Centre of Gravity
898,90
178,07
145,97
1029,41
1030,46
1051,46
720,77
1335,16
509,24
939,71
1090,86
718,35
1173,07
697,25
428,51
912,23
655,77
501,52
169,58
1468,75
950,83
623,38
822,99
654,77
258,16
572,08
1646,56
1089,48
327,48
921,52
487,30
265,65
382,17
1408,36
1491,21
595,85
591,71
1398,26
893,96
360,87
991,35
975,36
1098,99
1111,99
716,11
2703,48
870,91
541,51
714,21
420,83
533,15
986,42
2345,67
872,01
465,71
791,16
760,12
400,04
613,99
996,63
1436,34
459,97
612,17
1084,42
288,95
460,72
676,14
920,68
559,72
917,12
654,61
628,13
649,29
801,54
627,70
643,82
1645,26
564,44
1810,13
760,26
1969,52
1012,58
741,79
1214,68
1421,91
363,07
1243,88
1178,70
4398,49
768,64
956,26
1339,44
531,69
521,84
1159,34
539,11
399,57
469,95
1390,17
171,06
Valence
Intensity
Reaction time
Average Standard Deviation Average Standard Deviation Average Standard Deviation
-9,80
24,60
60,31
28,28
5,52
1,89
-29,73
18,99
34,86
24,23
5,19
1,53
-34,35
18,21
52,14
33,33
4,45
1,79
6,68
25,27
53,03
24,70
5,18
1,61
-7,89
30,96
61,92
22,66
5,09
2,53
5,09
18,02
51,91
27,18
5,31
1,84
0,97
25,28
49,55
22,44
5,39
2,04
8,53
24,83
61,94
22,47
5,19
1,84
11,03
24,75
43,26
27,94
4,81
1,74
15,91
24,51
65,58
24,73
5,00
1,84
-10,67
26,35
57,17
28,00
5,04
1,71
-21,34
26,69
67,51
23,66
4,47
1,87
-2,76
24,30
65,29
22,52
5,40
2,36
6,14
24,81
57,17
26,72
5,57
1,80
-19,00
21,02
34,78
24,17
5,52
2,15
3,92
23,01
63,06
23,30
4,79
2,00
16,68
26,43
71,37
21,32
5,22
2,35
1,86
18,32
30,57
26,53
5,87
1,62
-22,97
26,66
38,81
29,59
5,01
1,93
-20,53
20,61
49,50
28,55
5,10
1,76
1,97
14,51
43,03
23,70
5,29
2,31
-4,69
20,35
42,81
21,57
5,34
1,86
-17,29
22,41
60,24
24,91
4,95
1,84
0,42
15,83
50,00
28,58
5,10
1,90
-8,62
26,59
36,11
24,64
5,88
1,92
-20,38
23,91
55,70
28,15
5,83
2,25
31,32
18,59
78,14
21,19
4,21
1,89
-0,03
26,23
71,47
25,49
5,59
2,14
-11,97
27,80
49,74
25,91
5,11
1,89
-6,75
18,51
41,64
25,13
5,32
1,77
-3,83
26,96
44,94
27,54
5,40
1,69
-36,42
12,83
52,05
32,18
4,90
2,36
-32,95
16,13
63,05
27,94
4,75
1,96
23,14
21,87
65,97
26,71
4,85
2,31
11,18
17,39
51,35
27,27
5,40
1,86
0,68
29,95
65,46
25,11
4,74
2,01
6,53
24,76
52,86
25,83
5,94
2,45
19,42
23,83
68,83
23,58
5,18
2,08
-18,25
25,70
63,36
28,00
5,40
1,89
-0,89
31,18
49,64
27,57
5,55
2,08
-2,95
28,84
74,05
23,74
5,23
2,18
10,92
18,41
55,94
25,60
5,35
2,23
23,05
18,06
69,68
27,30
5,13
2,26
20,64
23,82
68,44
28,50
5,35
2,47
0,18
23,18
58,61
26,27
5,51
1,73
-2,23
33,06
73,97
24,85
5,62
2,21
-8,31
21,60
56,43
24,18
4,88
1,79
-9,74
24,68
21,50
20,07
5,74
2,17
22,56
22,97
71,28
24,94
4,92
2,11
-34,17
16,78
56,00
30,20
4,36
1,79
-32,58
21,25
61,14
25,28
4,49
2,04
2,22
21,24
47,94
31,37
5,38
1,70
-33,97
17,84
66,82
27,51
5,27
2,07
2,80
20,04
59,63
24,20
5,27
1,86
-27,00
20,94
56,32
27,46
4,81
2,31
-22,73
23,38
69,92
19,40
4,75
1,97
-7,17
18,58
48,97
26,26
5,75
1,81
-29,78
18,51
47,38
29,46
5,03
2,10
-2,86
23,69
56,63
24,93
4,71
1,89
4,97
29,10
57,23
26,40
5,00
2,03
16,00
23,63
55,43
26,57
5,51
2,06
-15,20
32,15
23,29
23,58
5,45
1,79
21,26
19,86
41,66
30,37
5,33
1,82
5,81
21,14
60,17
26,04
4,85
2,21
-29,35
22,51
48,97
31,09
4,85
2,02
16,22
21,34
42,05
24,71
5,11
1,91
-14,26
24,25
54,67
25,55
5,29
2,28
11,54
27,53
68,22
23,71
4,95
1,40
9,70
26,19
62,76
26,92
4,68
1,67
0,30
22,73
60,62
23,39
5,38
2,16
-6,17
25,15
44,66
25,60
5,36
1,93
-2,92
25,64
31,39
25,43
5,92
1,94
-14,94
22,67
31,47
25,03
5,36
2,21
0,34
25,34
42,00
25,25
5,85
1,66
-6,45
28,96
56,15
26,87
5,43
1,78
6,43
27,63
64,51
24,47
4,73
1,96
-3,51
26,89
71,30
23,85
4,81
2,00
1,78
21,87
49,58
22,92
5,21
2,04
-26,29
25,11
69,03
24,71
5,73
2,09
-10,08
26,24
57,69
25,58
4,91
1,82
7,89
21,35
54,30
33,49
5,01
1,58
8,30
20,92
52,24
25,24
5,05
2,20
-12,57
30,00
63,86
28,60
4,85
2,01
-7,67
27,15
62,55
26,28
5,39
2,16
12,15
24,21
51,35
26,72
5,51
2,13
-13,56
26,68
45,25
19,85
5,31
1,50
-0,69
27,89
68,42
27,08
5,46
1,97
-2,46
25,94
56,51
28,28
5,27
1,97
-24,06
27,62
67,53
28,29
5,02
2,26
1,73
17,76
47,06
24,34
5,05
1,84
9,89
17,93
57,82
31,37
5,36
1,90
7,84
24,19
54,58
31,69
5,68
1,93
-7,08
27,05
67,97
24,19
5,01
1,89
0,70
27,33
43,14
28,32
5,43
1,97
-1,47
24,28
58,61
25,87
4,94
1,88
5,35
20,61
29,24
25,89
5,73
1,55
-12,41
28,76
58,94
29,06
5,18
2,09
10,34
26,81
56,18
27,22
5,52
1,94
4,47
25,44
63,92
30,24
4,88
1,70
-34,77
19,42
53,15
31,16
4,92
2,19
hum_s001
hum_s002
hum_s003
hum_s004
hum_s005
hum_s006
hum_s007
hum_s008
hum_s009
hum_s010
hum_s011
hum_s012
hum_s013
hum_s014
hum_s015
hum_s016
hum_s017
hum_s018
hum_s019
hum_s020
hum_s021
hum_s022
hum_s023
hum_s024
hum_s025
hum_s026
hum_s027
hum_s028
hum_s029
hum_s030
hum_s031
hum_s032
hum_s033
hum_s034
hum_s035
hum_s036
hum_s037
hum_s038
hum_s039
hum_s040
hum_s041
hum_s042
hum_s043
hum_s044
hum_s045
hum_s046
hum_s047
hum_s048
hum_s049
hum_s050
hum_s051
hum_s052
hum_s053
hum_s054
hum_s055
hum_s056
hum_s057
hum_s058
hum_s059
hum_s060
hum_s061
hum_s062
hum_s063
hum_s064
hum_s065
hum_s066
hum_s067
hum_s068
hum_s069
hum_s070
hum_s071
hum_s072
hum_s073
hum_s074
hum_s075
hum_s076
hum_s077
hum_s078
hum_s079
hum_s080
hum_s081
hum_s082
hum_s083
hum_s084
hum_s085
hum_s086
hum_s087
hum_s088
hum_s089
hum_s090
hum_s091
hum_s092
hum_s093
hum_s094
hum_s095
hum_s096
hum_s097
hum_s098
hum_s099
hum_s100
Laugh
Erotic moan
Erotic moan
Erotic moan
Erotic moan
Erotic moan
Erotic moan
Erotic moan
Erotic moan
Laugh
Laugh
Laugh
Laugh
Cough
Cough
Cough
Cough
Cry
Cry
Yawn
Scream
Cry
Cry
Cry
Scream
Cough
General
Laugh
Laugh
Cough
General
General
General
Sigh
General
Scream
Laugh
General
Cry
Cry
Cough
Human moan
Laugh
General
Cry
General
Laugh
Laugh
Shout
Sigh
Laugh
General
General
Human moan
Laugh
General
Sigh
Cry
General
General
Retch
Laugh
Retch
Sigh
Cry
Laugh
Sigh
General
Cough
General
General
Human moan
Cough
General
Retch
General
Cough
Laugh
General
General
General
Yawn
Human moan
Human moan
Shout
Shout
Human moan
Cry
Human moan
Cry
Shout
Cry
Shout
Cry
Shout
Cry
Human moan
Human moan
Cry
Scream
7
2
2
2
2
1
1
4
2
8
12
9
11
3
4
5
6
4
1
2
1
2
1
2
1
2
3
6
9
3
3
6
1
1
2
1
10
2
2
5
4
1
11
1
1
4
7
5
3
2
5
3
3
3
5
1
1
3
1
1
1
6
3
2
1
11
1
1
4
2
2
1
3
3
3
1
3
6
2
1
3
1
1
1
1
1
1
5
1
6
1
7
1
5
1
7
1
1
3
1
0,11
0,69
0,54
0,54
0,73
2,00
2,00
0,35
0,57
0,22
0,12
0,16
0,16
0,50
0,38
0,38
0,23
0,41
2,00
0,79
2,00
0,77
1,14
0,85
0,93
0,52
0,64
0,18
0,22
0,25
0,63
0,29
1,13
1,32
0,79
1,49
0,11
0,75
0,90
0,36
0,31
0,42
0,13
2,00
2,00
0,22
0,14
0,19
0,60
0,84
0,25
0,48
0,32
0,58
0,30
2,00
1,82
0,64
1,90
2,00
0,72
0,24
0,59
0,84
1,59
0,18
1,51
2,00
0,23
0,94
1,00
0,96
0,34
0,59
0,61
1,60
0,47
0,23
0,59
2,00
0,54
1,28
0,95
0,58
1,10
0,75
1,31
0,39
0,67
0,30
1,14
0,25
1,08
0,40
0,70
0,20
1,72
1,33
0,41
1,29
0,03
0,11
0,19
0,19
0,01
0,04
0,17
0,09
0,03
0,05
0,09
0,13
0,07
0,20
0,08
0,36
0,37
0,55
0,01
0,08
0,22
0,13
0,05
0,01
0,60
0,11
0,11
0,03
0,07
0,72
0,16
0,05
0,05
0,04
0,05
0,13
0,17
0,12
0,09
0,19
0,18
0,17
0,17
0,37
0,23
0,42
0,03
0,02
0,05
0,12
0,38
0,04
0,10
0,19
0,12
0,12
0,02
0,08
0,19
0,13
0,03
0,17
0,03
0,12
600,40
264,71
315,82
342,73
377,25
228,78
241,47
200,03
263,66
454,44
518,55
160,75
183,01
256,33
294,67
442,07
439,87
467,61
635,39
293,33
878,43
404,97
529,80
505,61
1135,41
152,48
273,56
219,87
288,90
348,48
448,89
480,12
557,12
259,81
248,16
857,56
1183,74
270,05
427,03
585,81
830,81
281,87
284,77
305,72
303,53
360,18
436,83
342,34
489,26
246,41
412,10
332,76
257,04
282,03
705,95
349,97
255,23
367,09
293,87
282,38
228,43
415,53
328,12
306,85
366,50
204,97
144,10
165,29
247,53
152,91
147,76
204,87
205,76
355,58
150,09
114,20
231,85
357,92
300,29
148,80
124,30
179,29
162,56
217,74
184,65
293,19
392,60
480,34
187,10
216,06
314,62
400,86
352,77
384,42
279,79
154,52
211,01
396,03
387,54
540,61
103,01
15,48
22,74
1,30
2,91
21,04
0,38
52,27
169,22
14,52
37,55
32,09
48,22
19,89
40,97
24,03
14,23
0,62
12,96
31,76
12,69
5,80
35,68
12,75
107,57
81,66
85,59
153,07
2,80
68,13
68,04
68,32
40,39
12,93
30,40
69,71
119,89
24,23
71,18
49,15
10,42
21,66
34,74
12,26
67,98
31,07
35,65
18,37
0,00
41,02
1,76
21,85
43,97
27,44
48,07
232,85
61,32
10,81
27,11
35,55
69,56
26,86
13,82
4,36
9,18
14,05
15,67
14,86
17,33
19,64
5,35
3,74
4,83
14,16
8,41
1,35
2,67
6,12
4,75
6,47
8,62
11,73
19,29
20,51
4,93
15,11
16,03
10,81
23,97
1,77
12,58
10,04
7,24
0,68
20,16
31,93
14,50
9,76
2,23
25,61
18,60
25,63
13,66
24,78
1,47
10,77
7,73
22,35
5,36
13,32
10,15
20,09
21,89
0,87
10,64
20,54
20,56
8,42
21,88
22,91
7,17
16,85
14,46
5,99
18,80
4,32
3,71
2,80
15,06
5,60
11,41
16,39
0,83
19,81
11,08
26,63
4,55
12,28
2,08
17,41
5,17
6,65
18,87
13,23
16,28
19,82
17,55
3,91
14,82
20,38
16,92
14,90
15,45
13,66
17,12
5,96
20,48
11,29
7,45
5,89
24,29
33,25
15,87
10,04
4,40
1,45
5,19
4,09
3,51
3,11
2,33
3,54
4,69
2,07
3,24
1,42
2,45
1,56
1,97
7,05
6,21
6,98
2,53
0,98
4,88
4,03
4,41
4,59
2,36
2,65
2,07
4,09
0,94
1,69
8,91
0,50
5,39
11,59
5,53
7,02
0,96
0,24
4,71
2,14
5,26
3,40
7,50
2,33
4,48
0,26
2,77
2,48
0,63
0,72
2,82
0,92
2,13
0,43
3,84
2,52
1,99
0,43
4,22
5,02
2,48
2,06
1,65
4,80
1478,64
658,97
740,09
829,60
985,91
393,61
512,18
749,51
595,95
1072,71
1265,20
1104,23
783,91
704,38
722,96
888,88
1008,56
1032,81
1481,57
497,33
1640,00
1632,77
1319,02
1480,03
1275,95
1455,15
800,27
420,99
1039,83
1940,91
617,64
498,19
1458,93
804,34
2237,11
2234,08
1550,33
307,83
1347,18
891,36
2519,13
1655,68
1285,75
750,03
2486,08
1439,66
1715,26
508,01
1274,35
1750,71
1226,69
470,43
426,85
994,63
980,77
1534,29
717,60
1676,10
745,92
789,45
803,18
1525,01
1544,29
2266,82
2737,72
1244,59
462,62
1174,17
2390,69
638,92
1042,23
588,75
1986,88
1609,90
1765,37
1233,31
1717,98
1018,98
342,63
1663,24
504,42
441,16
885,31
1307,30
1073,91
1124,55
875,06
898,50
736,26
805,86
1195,72
1127,62
1332,21
1041,00
1352,49
697,06
1092,35
1312,72
1031,79
1109,26
36,59
33,59
23,81
32,95
26,56
17,89
16,65
19,95
15,32
35,92
40,82
34,95
31,45
-11,22
-15,66
-17,31
-13,97
-33,44
-38,85
0,26
-38,44
-6,84
-19,16
-29,21
-42,36
-13,82
-0,43
37,41
35,59
-14,59
29,34
30,39
37,36
-1,55
-4,29
-29,05
23,82
12,68
-37,68
-36,22
-12,21
3,11
34,47
19,43
-39,41
15,95
36,33
23,05
23,94
1,32
35,87
32,53
-10,44
16,37
35,11
10,34
18,05
-35,47
13,97
11,15
-15,08
31,72
-26,49
14,95
-35,50
35,82
-3,39
27,86
-12,82
-1,24
14,77
-9,74
-13,29
-13,30
-28,21
12,61
-9,86
34,46
18,00
7,55
19,63
0,31
-22,33
-32,84
11,64
-29,89
-9,70
-39,39
-14,34
-21,74
-32,26
-35,03
-9,42
-35,97
-25,41
-33,26
-30,54
-29,32
-36,82
-28,27
11,43
14,13
28,05
22,52
29,75
25,00
21,10
25,02
25,49
14,66
9,92
16,99
21,19
16,68
16,35
18,33
17,74
13,95
10,89
14,68
14,79
33,57
25,20
22,92
9,46
18,21
23,03
13,32
11,98
18,00
15,56
14,73
10,45
22,75
20,36
18,58
29,36
20,60
12,06
14,28
16,01
17,46
18,23
26,04
12,31
22,85
15,86
22,50
20,87
30,30
11,56
14,92
23,52
17,93
12,58
18,22
23,00
11,45
18,66
21,43
27,43
13,29
19,06
22,26
17,89
17,42
23,34
22,41
18,37
11,72
21,46
20,14
18,93
21,66
21,23
18,80
16,77
10,33
17,90
19,50
20,36
10,24
21,73
15,52
25,30
19,75
35,81
11,68
20,39
28,38
15,59
20,26
27,02
11,52
22,91
16,21
17,31
22,96
12,87
24,88
51,21
69,24
66,95
69,95
74,72
22,47
27,05
59,57
55,68
56,41
50,95
34,72
34,16
20,03
22,94
29,33
22,30
54,36
70,08
5,68
76,46
37,57
42,37
59,89
84,54
29,16
21,23
28,05
34,15
27,13
34,82
17,92
33,13
13,11
31,61
56,36
58,79
21,08
67,70
47,97
22,72
26,71
52,58
27,59
76,15
32,57
51,21
33,67
49,72
38,11
33,82
20,97
25,95
55,87
52,53
31,11
14,90
46,58
29,32
33,56
19,29
41,86
26,49
44,92
59,08
36,90
10,00
31,14
17,67
13,76
23,82
22,24
16,79
30,73
23,15
22,42
22,84
39,77
24,97
24,79
15,47
7,82
20,92
39,39
46,56
66,78
46,95
56,29
21,95
34,36
62,55
59,41
48,79
71,50
65,73
47,56
18,03
52,24
43,00
62,16
33,29
29,50
29,83
28,39
23,76
28,68
30,20
34,59
33,08
31,18
30,21
29,58
26,49
22,99
28,46
30,28
21,15
24,91
24,87
7,93
27,27
26,53
26,12
28,07
21,92
22,93
15,34
24,61
28,46
24,85
25,76
17,12
25,88
16,88
17,27
32,43
30,41
19,51
27,11
33,35
23,11
29,36
29,49
24,42
28,23
24,91
30,41
27,55
30,22
29,14
26,01
18,05
23,88
29,59
28,40
25,36
20,30
26,86
23,45
25,44
20,99
25,18
23,81
31,72
29,59
27,28
13,66
28,27
21,29
15,20
24,58
17,52
19,02
24,09
25,60
24,01
24,77
32,21
20,07
23,43
15,08
10,81
24,09
30,39
27,81
27,30
29,59
32,08
17,71
30,17
29,82
28,57
30,19
30,22
29,44
33,15
21,80
28,31
31,70
28,61
4,09
5,14
4,82
5,03
5,08
4,70
5,49
5,60
4,81
4,73
4,26
4,58
4,20
3,91
3,91
4,55
4,07
4,88
4,09
4,91
4,56
5,22
4,48
5,14
3,99
4,71
5,05
4,47
4,54
3,75
4,54
5,06
4,39
4,37
5,35
5,09
4,22
5,26
4,30
4,01
4,23
4,51
4,28
4,54
4,37
4,53
4,46
4,89
5,04
5,40
4,53
5,29
5,48
5,31
4,81
5,74
4,60
4,74
4,40
4,64
4,06
4,33
5,07
5,72
3,60
4,45
3,96
5,25
4,14
5,36
4,60
4,16
4,01
5,15
5,13
5,30
4,51
3,64
5,00
4,96
4,96
4,14
4,76
4,29
4,51
4,22
5,16
4,64
4,24
5,01
4,27
4,75
4,66
4,13
4,69
4,59
4,64
4,71
3,93
4,94
1,37
2,03
1,33
2,16
2,09
2,08
2,04
2,24
1,71
1,65
1,93
2,20
1,72
1,67
1,54
1,82
1,91
1,80
1,63
1,92
1,58
1,91
1,46
1,95
2,29
1,80
2,06
1,91
1,97
1,64
1,90
1,72
1,70
1,86
1,57
1,84
1,54
2,18
1,88
1,55
1,59
2,08
1,90
1,50
2,16
1,54
1,44
1,87
1,74
2,06
1,75
1,85
2,11
1,67
2,04
2,09
1,89
1,68
1,58
1,64
1,71
2,03
1,45
2,10
1,31
1,69
1,62
2,02
1,97
2,16
2,16
1,57
1,85
1,55
1,83
1,73
2,22
1,70
1,87
1,81
1,74
1,48
2,11
1,89
1,62
1,98
2,00
2,08
1,86
2,05
1,64
2,13
1,64
1,55
1,82
1,44
1,51
2,11
1,54
1,77
testid
7
8
11
24
29
30
32
33
34
37
43
54
56
63
67
71
76
79
82
84
88
90
96
103
108
109
110
111
113
114
115
121
123
129
130
132
135
139
140
Gender Age Dog ownership
female
29 haddog
female
28 never
female
24 hasdog
female
29 never
female
25 never
male
27 hasdog
male
28 never
female
25 never
female
42 hadfamilydog
female
25 hasfamilydog
female
14 hasdog
female
29 never
female
25 hasdog
male
31 hasdog
female
43 hasdog
female
23 hasdog
female
58 hasdog
female
30 haddog
female
31 hasdog
female
29 hasdog
female
28 hasdog
female
27 hasdog
female
25 hasfamilydog
female
55 hasdog
female
25 hasfamilydog
female
54 hasdog
female
20 hasfamilydog
female
25 hasdog
female
27 haddog
female
34 hasdog
female
30 hasdog
male
30 hadfamilydog
male
30 never
female
27 hasfamilydog
male
32 hadfamilydog
female
29 never
female
37 hasdog
female
37 hasdog
female
30 hasdog
context
asking for toy
begging for food
before walk
bored
before snow shoveling
foodguarding
greeting
neutral
petting
play
pup before feeding
separation
dynamic threatening
asked to speak
stranger at fence
threatening stranger
assumed valence
+
+
+
+
+
+
+
-
aggreement
94%
63%
88%
94%
75%
94%
100%
75%
100%
100%
69%
94%
100%
63%
100%
100%
View publication stats