‘Beware, I am big and non-dangerous!’ – Playfully growling dogs are perceived larger than their actual size by their canine audience

Bálint, Anna; Faragó, Tamás; Dóka, Antal; Miklósi, Ádám; Pongrácz, Péter

doi:10.1016/j.applanim.2013.07.013

Cited by 19 publications

(19 citation statements)

References 63 publications

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“…Interestingly, horses looked preferentially at the incongruent picture, which is in contrast to the results found in primates and dogs in similar paradigm [10,17,18,19,20,21,22]. This result might be explained by differences in how these different species processed the task.…”

Section: Discussioncontrasting

confidence: 79%

“…Subjects succeed by discriminating between the two pictures based on their congruency with the vocalization. For instance, dogs have been shown to match the size of a conspecific or the gender of a human experimenter with their voice [17,18,19]. To date, chimpanzees, rhesus monkeys, capuchin monkeys and dogs have been shown to cross-modally recognize conspecifics’ emotions using this paradigm [10,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

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Horses Categorize Human Emotions Cross-Modally Based on Facial Expression and Non-Verbal Vocalizations

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Cuzol

Parias

et al. 2019

Animals

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Simple SummaryRecently, an increasing number of studies have investigated the expression and perception of emotions by non-human animals. In particular, it is of interest to determine whether animals can link emotion stimuli of different modalities (e.g., visual and oral) based on the emotions that are expressed (i.e., to recognize emotions cross-modally). For domestic species that share a close relationship with humans, we might even wonder whether this ability extends to human emotions. Here, we investigated whether domestic horses recognize human emotions cross-modally. We simultaneously presented two animated pictures of human facial expressions, one typical of joy and the other of anger; simultaneously, a speaker played a human non-verbal vocalization expressing joy or anger. Horses looked at the picture that did not match the emotion of the vocalization more (probably because they were intrigued by the paradoxical combination). Moreover, their behavior and heart rate differed depending on the vocalization: they reacted more negatively to the anger vocalization and more positively to the joy vocalization. These results suggest that horses can match visual and vocal cues for the same emotion and can perceive the emotional valence of human non-verbal vocalizations.AbstractOver the last few years, an increasing number of studies have aimed to gain more insight into the field of animal emotions. In particular, it is of interest to determine whether animals can cross-modally categorize the emotions of others. For domestic animals that share a close relationship with humans, we might wonder whether this cross-modal recognition of emotions extends to humans, as well. In this study, we tested whether horses could recognize human emotions and attribute the emotional valence of visual (facial expression) and vocal (non-verbal vocalization) stimuli to the same perceptual category. Two animated pictures of different facial expressions (anger and joy) were simultaneously presented to the horses, while a speaker played an emotional human non-verbal vocalization matching one of the two facial expressions. Horses looked at the picture that was incongruent with the vocalization more, probably because they were intrigued by the paradoxical combination. Moreover, horses reacted in accordance with the valence of the vocalization, both behaviorally and physiologically (heart rate). These results show that horses can cross-modally recognize human emotions and react emotionally to the emotional states of humans, assessed by non-verbal vocalizations.

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Section: Discussioncontrasting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Horses Categorize Human Emotions Cross-Modally Based on Facial Expression and Non-Verbal Vocalizations

Trösch

Cuzol

Parias

et al. 2019

Animals

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“…Still it remains possible that dogs did solve the tasks both in the studies of Péter et al (2013) and the Pongrácz et al (2003) by utilising only local enhancement cues and without perceiving the content of the videos (e.g., by simply going to that location where they previously detected motion). On the other hand, several other studies gave evidence that dogs perceive still pictures according to the content it depicts (e.g., photographs of dogs – Bálint, Faragó, Dóka, & Miklósi, 2013; Faragó et al, 2010; pictures of humans – Adachi, Kuwahata, & Fujita, 2007; Albuquerque et al, 2016; Nagasawa, Murai, Mogi, & Kikusui, 2011). An especially interesting study from this aspect was performed by Müller and colleagues (2015), who showed that dogs can discriminate between human faces showing anger or happiness based on still pictures shown on a touch screen device.…”

Section: Introductionmentioning

confidence: 99%

Familiarity with images affects how dogs (Canis familiaris) process life-size video projections of humans

Pongrácz

Andreas

Miklósi

2018

Quarterly Journal of Experimental Psychology

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A central problem of behavioural studies providing artificial visual stimuli for non-human animals is to determine how subjects perceive and process these stimuli. Especially in the case of videos, it is important to ascertain that animals perceive the actual content of the images and are not just reacting to the motion cues in the presentation. In this study, we set out to investigate how dogs process life-sized videos. We aimed to find out whether dogs perceive the actual content of video images or whether they only react to the videos as a set of dynamic visual elements. For this purpose, dogs were presented with an object search task where a life-sized projected human was hiding a target object. The videos were either normally oriented or displayed upside down, and we analysed dogs' reactions towards the projector screen after the video presentations, and their performance in the search task. Results indicated that in the case of the normally oriented videos, dogs spontaneously perceived the actual content of the images. However, the 'Inverted' videos were first processed as a set of unrelated visual elements, and only after some exposure to these videos did the dogs show signs of perceiving the unusual configuration of the depicted scene. Our most important conclusion was that dogs process the same type of artificial visual stimuli in different ways, depending on the familiarity of the depicted scene, and that the processing mode can change with exposure to unfamiliar stimuli.

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“…The ethological analysis of the possible functions of canine vocalizations has so far provided data about the individual-specific content of wolf howls (Mazzini et al 2013;Root-Gutteridge et al 2013), the indexical content of dog growls, related to the caller's body size (Taylor et al 2008(Taylor et al , 2010Farago´ et al 2010a;Ba´lint et al 2013), and the context-specific content of dog growls (Farago´ et al 2010b;Taylor et al 2009). However, even though barking is considered to be the most characteristic form of dog vocalization, exceeding the barks of wolves and coyotes both in its frequency of occurrence and variability (Cohen and Fox 1976), the functional aspects of dog barks are surprisingly little known.…”

Section: Introductionmentioning

confidence: 99%

Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking

et al. 2014

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Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is known about its role in the intraspecific communication of this species. Besides the obvious need for ethological research, both in the field and in the laboratory, the possible information content of barks can also be explored by computerized acoustic analyses. This study compares four different supervised learning methods (naive Bayes, classification trees, k-nearest neighbors and logistic regression) combined with three strategies for selecting variables (all variables, filter and wrapper feature subset selections) to classify Mudi dogs by sex, age, context and individual from their barks. The classification accuracy of the models obtained was estimated by means of K-fold cross-validation. Percentages of correct classifications were 85.13 % for determining sex, 80.25 % for predicting age (recodified as young, adult and old), 55.50 % for classifying contexts (seven situations) and 67.63 % for recognizing individuals (8 dogs), so the results are encouraging. The best-performing method was k-nearest neighbors following a A. Larranaga Student at the Universidad Alfonso X El Sabio, Av. Universidad, 1, 28691 Villanueva de la Canada, Madrid, Spain wrapper feature selection approach. The results for classifying contexts and recognizing individual dogs were better with this method than they were for other approaches reported in the specialized literature. This is the first time that the sex and age of domestic dogs have been predicted with the help of sound analysis. This study shows that dog barks carry ample information regarding the caller's indexical features. Our computerized analysis provides indirect proof that barks may serve as an important source of information for dogs as well.

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‘Beware, I am big and non-dangerous!’ – Playfully growling dogs are perceived larger than their actual size by their canine audience

Cited by 19 publications

References 63 publications

Horses Categorize Human Emotions Cross-Modally Based on Facial Expression and Non-Verbal Vocalizations

Horses Categorize Human Emotions Cross-Modally Based on Facial Expression and Non-Verbal Vocalizations

Familiarity with images affects how dogs (Canis familiaris) process life-size video projections of humans

Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking

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