1 2 Humans rely on the same rules to assess emotional valence and intensity in conspecific and dog vocalizations 3 Tamás Faragó1*, Attila Andics1, Viktor Devecseri1, Anna Kis2,3, Márta Gácsi1, Ádám Miklósi1,2 4 1 5 2 6 7 3 8 *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. 9 10 11 12 13 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. 14 15 16 17 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. 18 19 20 21 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. 22 23 Keywords: dog; human; vocal communication; emotion valence assessment; emotion intensity assessment; nonverbal emotion expressions 24 25 26 27 28 29 30 31 32 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]. 33 34 35 36 37 38 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 39 40 41 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. 42 43 44 45 46 47 48 49 50 51 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. 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 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]. 67 68 69 70 71 72 73 74 75 76 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). 77 78 79 80 81 82 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 83 84 85 86 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. 87 88 89 90 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). 91 92 93 94 95 96 97 98 99 100 101 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). 102 103 104 105 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. 106 107 108 109 110 111 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. 112 113 114 115 116 117 118 119 120 121 122 123 124 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- 125 126 127 128 129 130 131 132 133 134 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. 135 136 137 138 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. 139 140 141 142 143 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. 144 7 145 146 1 Nesse, R. M. 1990 Evolutionary explanations of emotions. Hum. Nat. 1, 261–289. (doi:10.1007/BF02733986) 147 148 149 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) 150 151 152 3 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) 153 154 4 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) 155 156 5 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) 157 158 159 160 161 6 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. 162 163 7 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 164 165 8 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) 166 167 168 9 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) 169 170 10 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) 171 172 173 11 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) 174 175 176 12 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) 177 178 13 Russell, J. A. 1980 A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178. (doi:10.1037/h0077714) 179 180 14 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) 181 182 183 184 185 186 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. 187 188 189 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) 190 191 192 193 194 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. 195 196 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 -0.282 197 198 199 10 Short title Vocal emotion assessment by humans -2.951 0.00 1 Supplementary methods 2 Samples 3 4 5 6 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. 7 8 9 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). 10 11 12 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). 13 Acoustical measures 14 15 16 17 18 19 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. 20 Questionnaire 21 22 The webpage of the survey can be found following this link: http://www.inflab.bme.hu/~viktor/soundrating/index.html 23 24 (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) 25 Data analysis 26 27 28 29 30 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. 31 32 33 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). 34 35 36 37 38 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 39 40 compare the responses of dog owners and non-dog owners and also to test for possible gender differences. 41 42 43 44 45 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. 46 47 48 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. 49 Supplementary results 50 51 52 53 54 55 56 57 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). 58 59 60 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). 61 62 63 64 65 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). 66 67 68 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). 69 70 71 72 73 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. 74 Supplementary discussion 75 76 77 78 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 79 80 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. 81 82 83 84 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]. 85 86 87 88 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. 89 90 91 92 93 94 95 96 97 98 99 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]. 100 Supplementary references 101 102 103 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 7 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 8 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) 123 124 125 126 127 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