Emotional expressions play an important part in social communication of dogs and humans, but comparative studies between dogs and humans are rare. In addition, little is known about how emotions are perceived across species and how attention is divided among different parts of faces. Here, we compared the gazing behavior of dogs and adult humans toward emotional dog and human facial expressions. Dogs mostly gazed at the eye and nose areas, whereas the humans' focus also included the mouth area. The gazing behavior of both species was affected by the facial expressions and species presented, and they gazed at emotional more than neutral stimuli. Dogs and humans demonstrated attentional bias toward angry and happy eyes of their own species, which highlights the ecological salience of the species and the importance of eyes in reading conspecifics' emotional expressions. For both species, mouths of aggressive dogs were gazed at more than those of angry humans, whereas mouths of happy humans were gazed at more than happy dog mouths. Thus, the mouth area provides important emotional cues for recognizing emotions across species, suggesting the ecological salience of facial expressions connecting to the subcortical visual pathway across mammalian species. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Cardiac function is influenced by both physiological and emotional factors, with heart rate variability (HRV) serving as a key indicator of health and wellbeing. Despite the growing interest in utilizing short-term cardiac measures in canine science, the behavioral aspects of HRV in domestic dogs (Canis familiaris) remain largely unexplored. This study aimed to provide reference values for short-term HRV to aid future research on canine behavior in medium-sized, mesocephalic and dolichocephalic dogs, to examine differences in HRV during typical canine behaviors, and to develop practical tools for the analysis of canine cardiac function. We assessed heart rate, HRV, and physical activity of 29 dogs across five behavioral states (Resting, Playing, Panting, Spontaneous sniffing and Food searching) and investigated how these behaviors influenced time-domain and frequency-domain HRV parameters. The impact of physical activity, sex, neutered status, age, height, and weight on these parameters within the specific sample was also assessed. Both time-domain and frequency domain parameters were affected by the behaviors. Precisely, HRV generally decreased with behavior-related physical activity (root mean square of successive differences, RMSSD; Resting vs. Playing, p < 0.001; and Resting vs. Searching for food, p < 0.001). However, RMSSD was significantly lower during Searching for food compared to Spontaneous sniffing (p = 0.012), despite similar activity levels, indicating higher emotional arousal when searching for food. Overall, the high-frequency component (HF power) and RMSSD differentiated well between the distinct canine behaviors. Also, physical activity (measured as 3D acceleration) was the most influential background variable in this highly specific sample, correlating with HRV parameters and depending on the behavior.

Abstract Coordinated dynamics between individuals are a hallmark of social interaction, yet the temporal structure and physiological basis of such coupling beyond human species remain poorly understood. Here, we investigated cross-species biobehavioral synchrony by simultaneously quantifying motion dynamics and autonomic activity with hyperscanning of human–canine dyads. We observed both spontaneous and task-related synchrony across motion dynamics, heart rate, and heart rate variability at multiple timescales. Importantly, synchrony was modulated by individual and relational factors. Task-related autonomic synchrony was affected by the human temperament, whereas greater familiarity within the dyad altered the leader–follower dynamics, shifting directional influence from human-led toward canine-driven coordination. Motion synchrony emerged with minimal delay, whereas cardiac synchrony unfolded across longer timescales, suggesting coordinated processes underlying the shared activity, arousal, and autonomic regulation. Our findings extend current models of social synchrony beyond human interactions and reveal that regulatory dynamics underlying coordinated behavior operate across species boundaries.

Dexmedetomidine is widely used for sedation and alleviation of fear and anxiety in dogs, but its effects on cognitive processes are not known. We studied the effect of a low dose of dexmedetomidine on dogs' gazing behavior while they observed images of emotional facial expressions of conspecifics. Eight dogs were administered intramuscular dexmedetomidine (2.5 μg/kg) and saline-placebo in a randomized cross-over design. Images of neutral and aggressive dog faces were shown on a monitor, and dogs' eye movements were recorded with an eye gaze tracker. The dogs' behavior was evaluated using three sub-scores: motivation, level of sedation and activity. Friedman test and Wilcoxon signed-rank test were used to analyze the data. The dogs were able to walk without visible ataxia after both treatments. When aggressive dog faces were shown, the observer dogs made fewer (p = 0.036) and shorter (p = 0.025) fixations after dexmedetomidine than placebo treatment. Furthermore, after receiving dexmedetomidine, dogs gazed less at aggressive than neutral dog ears compared with the placebo treatment (p = 0.036). In behavior scores, the only significant difference between treatments was detected in activity score: dogs were less active after dexmedetomidine than placebo (p = 0.026). In conclusion, a low dose of dexmedetomidine reduced dogs' sustained attention to aggressive conspecific faces, while leaving core face-scanning strategies and cognitive engagement intact. These findings provide objective support for dexmedetomidine's anxiolytic profile and indicate that eye-tracking is a sensitive tool for assessing how pharmacological treatments influence socio-emotional attention in dogs.

Despite the growing interest in the nonhuman animal emotionality, we currently know little about the human brain processing of nonconspecific emotional expressions. Here, we characterized the millisecond-scale temporal dynamics of human brain responses to conspecific human and nonconspecific canine emotional facial expressions. Our results revealed generally similar cortical responses to human and dog facial expressions in the occipital cortex during the first 500 ms, temporal cortex at 100-500 ms and parietal cortex at 150-350 ms from the stimulus onset. Responses to dog faces were pronounced at the latencies in temporal cortices corresponding to the time windows of early posterior negativity and late posterior positivity, suggesting attentional engagement to emotionally salient stimuli. We also utilized support vector machine-based classifiers to discriminate between the brain responses to different images. The subject trait-level empathy correlated with the accuracy of classifying the brain responses of aggressive from happy dog faces and happy from neutral human faces. This result likely reflects the attentional enhancement provoked by the subjective ecological salience of the stimuli.

Abstract Domestic dog, Canis familiaris, is called “human’s best friend”: dogs are everywhere in the Western societies, and over 470 million dogs are kept as pets worldwide. But what are the social and emotional properties of dogs that enable such an affectionate friendship bond across species? During their domestication 14,000–30,000 years ago, dogs have undergone selective changes and developed behavioral skills that enable them to better function in human social groups. Humans and dogs share some basic emotional functionality of the nervous systems, which aids in interspecies interaction. Dogs have positive and negative affective states, with most research conducted on fear, anger/aggressiveness, reward-processing and joy. Still, dogs are not four-legged, nonverbal humans. In the light of scientific results, canine capability for social emotions such as guilt or jealousy appears limited. Dogs understand human behavior from a dog’s point of view, and humans understand dogs from a human’s point of view.

Undesirable behaviors of dogs and cats may reduce their quality of life and may cause harm to their owners. Such behaviors have been treated with medications as well as behavioral training and environmental modifications. Finnish veterinarians answered a web-based questionnaire to characterize the treatment of undesirable behaviors in dogs and cats by medications. Fourteen indications related to undesirable behaviors were defined in the questionnaire. Most psychoactive medications authorised for veterinary or human use in Finland and allowed to be prescribed for animal use according to the national legislation were listed. In addition, some sedative and analgesic agents were included. Canine and feline undesirable behaviors were treated by the respondents with a wide variety of medications. In many cases, none of the remedies used could be demonstrated to be a clear favourite for a certain indication. Off-label use of psychoactive medications was common. Many medications were used without research-based evidence of their efficacy for the indication in those species in question. Treatment of dogs was more often associated with advice for behavioral training in addition to medications than treatment of cats. Both the common off-label use of medications and the wide variety of substances used for the particular behavioral problem reveal the need for further clinical evidence of the efficacy of medications to treat various canine and feline undesirable behaviors.

Classifying behavior by tracking acceleration has received increased interest lately. Here, we evaluated the performance of three commercial activity trackers in differentiating seven dog behaviors. Adult companion dogs (N = 70) performed still (lying, sitting, standing) and dynamic (walking, sniffing, trotting, playing) tasks, while wearing ActiGraph GT9X Link, Kaunila and FitBark devices placed on the neck collar and ActiGraph GT9X Link placed on the back. Each task was performed for 3 min within a session and repeated in two sessions; the behaviors were confirmed from video recordings. Activity scores of devices were calculated as median values for behavioral differentiation, and as minute-based values for inter-device correlations and cutoff analysis. Measurements of all devices correlated with each other, and median activity scores of all devices - unaffected by dog age, weight or sex - differentiated the still from dynamic behaviors. Dynamic behaviors were also differentiated from each other, with exception of walking vs. sniffing by back-placed ActiGraph GT9X and Kaunila. The definition of cutoffs between behaviors varied from moderate to high accuracy; defined cutoffs for standing and walking were the least accurate. The classification performance of the cutoffs had an accuracy of 80% in all the devices; thus, they performed reasonably well in classifying these behaviors.

AbstractBehavioral and physiological synchrony facilitate emotional closeness in attachment relationships. The aim of this pseudorandomized cross-over study was to investigate the emotional and physiological link, designated as co-modulation, between dogs and their owners. We measured the heart rate variability (HRV) and physical activity of dogs belonging to co-operative breeds (n = 29) and their owners during resting baselines and positive interaction tasks (Stroking, Training, Sniffing, Playing) and collected survey data on owner temperament and dog–owner relationship. Although overall HRV and activity correlated between dogs and their owners across tasks, task-specific analyses showed that HRV of dogs and owners correlated during free behaving (Pre- and Post-Baseline), whereas the activity of dogs and owners correlated during predefined interaction tasks (Stroking and Playing). Dog overall HRV was the only predictive factor for owner overall HRV, while dog height, ownership duration, owner negative affectivity, and dog–owner interaction scale predicted dog overall HRV. Thus, the characteristics of dog, owner, and the relationship modified the HRV responses in dog–owner dyads. The physiology and behavior of dogs belonging to co-operative breeds and their owners were therefore co-modulated, demonstrating physiological and emotional connection comparable to those found in attachment relationships between humans.

Domestic dogs (Canis familiaris) have excellent olfactory processing capabilities that are utilized widely in human society e.g., working with customs, police, and army; their scent detection is also used in guarding, hunting, mold-sniffing, searching for missing people or animals, and facilitating the life of the disabled. Sniffing and searching for odors is a natural, species-typical behavior and essential for the dog's welfare. While taking advantage of this canine ability widely, we understand its foundations and implications quite poorly. We can improve animal welfare by better understanding their olfactory world. In this review, we outline the olfactory processing of dogs in the nervous system, summarize the current knowledge of scent detection and differentiation; the effect of odors on the dogs’ cognitive and emotional processes and the dog-human bond; and consider the methodological advancements that could be developed further to aid in our understanding of the canine world of odors.

Emotional facial expressions are an important part of across species social communication, yet the factors affecting human recognition of dog emotions have received limited attention. Here, we characterize the recognition and evaluation of dog and human emotional facial expressions by 4-and 6-year-old children and adult participants, as well as the effect of dog experience in emotion recognition. Participants rated the happiness, anger, valence, and arousal from happy, aggressive, and neutral facial images of dogs and humans. Both respondent age and experience influenced the dog emotion recognition and ratings. Aggressive dog faces were rated more often correctly by adults than 4-year-olds regardless of dog experience, whereas the 6-year-olds' and adults' performances did not differ. Happy human and dog expressions were recognized equally by all groups. Children rated aggressive dogs as more positive and lower in arousal than adults, and participants without dog experience rated aggressive dogs as more positive than those with dog experience. Children also rated aggressive dogs as more positive and lower in arousal than aggressive humans. The results confirm that recognition of dog emotions, especially aggression, increases with age, which can be related to general dog experience and brain structure maturation involved in facial emotion recognition.

As companion dogs spend most of their lives with humans, the human–dog relationship and owner temperament may affect the dog behavior. In this study (n = 440), we investigated the relationship between the dog owner temperament (ATQ-R), owner-perceived dog–owner relationship (MDORS) and the dog behavior in three behavioral tests: the object-choice test, the unsolvable task, and the cylinder test. Dog owner temperament influenced the dog–owner relationship. Owners with high negative affectivity showed higher emotional closeness and perceived costs of their dog, whereas owners with high effortful control showed lower emotional closeness and perceived costs. Higher dog activity during the behavioral tests was also connected with owner-perceived lower emotional closeness. Furthermore, dog breed group modulated the connection between the owner temperament and dog behavior. Owner’s high negative affectivity correlated with herding dogs’ lower scores in the object choice test, while the behavior of primitive type dogs was unaffected by the owner temperament. Our results confirm that human characteristics are associated with the owner-reported dog–owner relationship, and owner temperament may have a modulatory effect on the dog social and cognitive behavior depending on the dog breed group, which should be investigated further.

We evaluated the effect of the dog–owner relationship on dogs’ emotional reactivity, quantified with heart rate variability (HRV), behavioral changes, physical activity and dog owner interpretations. Twenty nine adult dogs encountered five different emotional situations (i.e., stroking, a feeding toy, separation from the owner, reunion with the owner, a sudden appearance of a novel object). The results showed that both negative and positive situations provoked signs of heightened arousal in dogs. During negative situations, owners’ ratings about the heightened emotional arousal correlated with lower HRV, higher physical activity and more behaviors that typically index arousal and fear. The three factors of The Monash Dog–Owner Relationship Scale (MDORS) were reflected in the dogs’ heart rate variability and behaviors: the Emotional Closeness factor was related to increased HRV (p = 0.009), suggesting this aspect is associated with the secure base effect, and the Shared Activities factor showed a trend toward lower HRV (p = 0.067) along with more owner-directed behaviors reflecting attachment related arousal. In contrast, the Perceived Costs factor was related to higher HRV (p = 0.009) along with less fear and less owner-directed behaviors, which may reflect the dog’s more independent personality. In conclusion, dogs’ emotional reactivity and the dog–owner relationship modulate each other, depending on the aspect of the relationship and dogs’ individual responsivity.

Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six degree-of-freedom movement sensors attached to the collar and the harness. With 17 dogs the collection procedure was repeated. The duration of each of the seven behaviors was approximately three minutes. The order of the tasks was varied between the dogs and the two repetitions (for the 17 dogs). The behaviors were annotated post-hoc based on the video recordings made with two camcorders during the tests with one second resolution. The annotations were accurately synchronized with the raw movement sensors data. The annotated data was originally used for training behavior classification machine learning algorithms for classifying the seven behaviors. The developed signal processing and classification algorithms are provided together with the raw measurement data and reference annotations. The description and results of the original investigation that the dataset relates to are found in: P. Kumpulainen, A. Valldeoriola Cardó, S. Somppi, H. Törnqvist, H. Väätäjä, P. Majaranta, Y. Gizatdinova, C. Hoog Antink, V. Surakka, M. V. Kujala, O. Vainio, A. Vehkaoja, Dog behavior classification with movement sensors placed on the harness and the collar, Applied Animal behavior Science, 241 (2021), 105,393.

Dog owners' understanding of the daily behaviour of their dogs may be enhanced by movement measurements that can detect repeatable dog behaviour, such as levels of daily activity and rest as well as their changes. The aim of this study was to evaluate the performance of supervised machine learning methods utilising accelerometer and gyroscope data provided by wearable movement sensors in classification of seven typical dog activities in a semi-controlled test situation. Forty-five middle to large sized dogs participated in the study. Two sensor devices were attached to each dog, one on the back of the dog in a harness and one on the neck collar. Altogether 54 features were extracted from the acceleration and gyroscope signals divided in two-second segments. The performance of four classifiers were compared using features derived from both sensor modalities. and from the acceleration data only. The results were promising; the movement sensor at the back yielded up to 91 % accuracy in classifying the dog activities and the sensor placed at the collar yielded 75 % accuracy at best. Including the gyroscope features improved the classification accuracy by 0.7-2.6 %, depending on the classifier and the sensor location. The most distinct activity was sniffing, whereas the static postures (lying on chest, sitting and standing) were the most challenging behaviours to classify, especially from the data of the neck collar sensor. The data used in this article as well as the signal processing scripts are openly available in Mendeley Data, https://doi.org/10.17632/vxhx934tbn.1.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background This study examines how dogs observe images of natural scenes containing living creatures (wild animals, dogs and humans) recorded with eye gaze tracking. Because dogs have had limited exposure to wild animals in their lives, we also consider the natural novelty of the wild animal images for the dogs. Methods The eye gaze of dogs was recorded while they viewed natural images containing dogs, humans, and wild animals. Three categories of images were used: naturalistic landscape images containing single humans or animals, full body images containing a single human or an animal, and full body images containing a pair of humans or animals. The gazing behavior of two dog populations, family and kennel dogs, were compared. Results As a main effect, dogs gazed at living creatures (object areas) longer than the background areas of the images; heads longer than bodies; heads longer than background areas; and bodies longer than background areas. Dogs gazed less at the object areas vs. the background in landscape images than in the other image categories. Both dog groups also gazed wild animal heads longer than human or dog heads in the images. When viewing single animal and human images, family dogs focused their gaze very prominently on the head areas, but in images containing a pair of animals or humans, they gazed more at the body than the head areas. In kennel dogs, the difference in gazing times of the head and body areas within single or paired images failed to reach significance. Discussion Dogs focused their gaze on living creatures in all image categories, also detecting them in the natural landscape images. Generally, they also gazed at the biologically informative areas of the images, such as the head, which supports the importance of the head/face area for dogs in obtaining social information. The natural novelty of the species represented in the images as well as the image category affected the gazing behavior of dogs. Furthermore, differences in the gazing strategy between family and kennel dogs was obtained, suggesting an influence of different social living environments and life experiences.

Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography (EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100–140 ms and 240–280 ms. We also detected a response sensitive to threatening dog faces at 30–40 ms; generally, responses differentiating emotional expressions were found at 130–170 ms, and differentiation of faces from objects occurred at 120–130 ms. The cortical sources underlying the highest-amplitude EEG signals were localized to the dog visual cortex.

In the target article, I called for a discussion on the nature and extent of dogs' emotions. The commentators generally agreed on the existence of dog emotions, but the diversity and quality of dog emotions, as well as the influence of human social cognition on perceiving dog emotions, raised more debate. To respond to the stimulating commentaries, I touch briefly on the philosophy of (canine) mind and discuss further the benefits of comparing cognition across species, secondary emotions, and the shaping of canine emotions by evolution, breeding and experience. I conclude with suggestions for future research guidelines on studies of canine emotion inspired by the discussion.