Head-mounted mobile eye-tracking in the domestic dog: A new method

Using mobile eye tracking to study dogs' understanding of human referential communication

The extent to which dogs understand human referential communication is among the most studied questions in canine cognition research. While it is widely accepted that dogs follow (some) human referential signals, the way they understand them remains controversial. Here, we applied mobile eye tracking with dogs to investigate during real-world interactions how ostensive pointing and gaze cues direct dogs' visual attention and bias their subsequent choices in an object-choice task. We addressed the question of whether dogs would exhibit a greater response to referential communication compared with other directional cues. Five conditions were tested (pointing, pointing + gazing, gazing, fake throwing and no-cue control), each cue condition indicating the location of a hidden food reward. Results demonstrated that the combination of pointing and gazing significantly increased dogs' attention towards the designated referent. In pointing + gazing, dogs maintained longer attention on the referent compared with other conditions and they approached it significantly above chance levels. While the alternative cue (fake throwing) moved the dogs' gaze to the indicated direction, it did not increase the frequency of gaze shifts to the precise referent location. Our findings highlight that the joint use of pointing and gazing is a particularly effective method for directing dogs' attention to a referent.

Conducting head-mounted eye-tracking research with young children with autism and children with increased likelihood of later autism diagnosis

BACKGROUND

Over the past years, researchers have been using head-mounted eye-tracking systems to study young children's gaze behaviors in everyday activities through which children learn about the world. This method has great potential to further our understanding of how millisecond-level gaze behaviors create multisensory experiences and fluctuate around social environments. While this line of work can yield insight into early perceptual experiences and potential learning mechanisms, the majority of the work is exclusively conducted with typically-developing children. Sensory sensitivities, social-communication difficulties, and challenging behaviors (e.g., disruption, elopement) are common among children with developmental disorders, and they may represent potential methodological challenges for collecting high-quality data.

RESULTS

In this paper, we describe our research practices of using head-mounted eye trackers with 41 autistic children and 17 children with increased likelihood of later autism diagnosis without auditory or visual impairments, including those who are minimally or nonspeaking and/or have intellectual disabilities. The success rate in gathering data among children with autism was 92.68%. 3 of 41 children failed to complete the play-session, resulting in an 86.36% success rate among 1-4-year-olds and a 100.00% success rate among 5-8-year-olds. 1 of 17 children with increased likelihood of later autism diagnosis failed to complete the play-session, resulting in a success rate of 94.11%. There were numerous "challenging" behaviors relevant to the method. The most common challenging behaviors included taking the eye-tracking device off, elopement, and becoming distressed. Overall, among children with autism, 88.8% of 1-4-year-olds and 29.4% of 5-8-year-olds exhibited at least one challenging behavior.

CONCLUSIONS

Research capitalizing on this methodology has the potential to reveal early, socially-mediated gaze behaviors that are relevant for autism screening, diagnosis, and intervention purposes. We hope that our efforts in documenting our study methodology will help researchers and clinicians effectively study early naturally-occuring gaze behaviors of children during non-experimental contexts across the spectrum and other developmental disabilities using head-mounted eye-tracking. Ultimately, such applications may increase the generalizability of results, better reflect the diversity of individual characteristics, and offer new ways in which this method can contribute to the field.

Why do dogs wag their tails?

Tail wagging is a conspicuous behaviour in domestic dogs (Canis familiaris). Despite how much meaning humans attribute to this display, its quantitative description and evolutionary history are rarely studied. We summarize what is known about the mechanism, ontogeny, function and evolution of this behaviour. We suggest two hypotheses to explain its increased occurrence and frequency in dogs compared to other canids. During the domestication process, enhanced rhythmic tail wagging behaviour could have (i) arisen as a by-product of selection for other traits, such as docility and tameness, or (ii) been directly selected by humans, due to our proclivity for rhythmic stimuli. We invite testing of these hypotheses through neurobiological and ethological experiments, which will shed light on one of the most readily observed yet understudied animal behaviours. Targeted tail wagging research can be a window into both canine ethology and the evolutionary history of characteristic human traits, such as our ability to perceive and produce rhythmic behaviours.

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English- language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Altered pupil responses to social and non-social stimuli in Shank3 mutant dogs

Pupillary response, an important process in visual perception and social and emotional cognition, has been widely studied for understanding the neural mechanisms of neuropsychiatric disorders. However, there have been few studies on pupil response to social and non-social stimuli in animal models of neurodevelopmental disorders including autism spectrum disorder (ASD) and attention deficit hyperactivity disorder. Here, we developed a pupilometer using a robust eye feature-detection algorithm for real-time pupillometry in dogs. In a pilot study, we found that a brief light flash induced a less-pronounced and slower pupil dilation response in gene-edited dogs carrying mutations in Shank3; mutations of its ortholog in humans were repeatedly identified in ASD patients. We further found that obnoxious, loud firecracker sound of 120 dB induced a stronger and longer pupil dilation response in Shank3 mutant dogs, whereas a high reward food induced a weaker pupillary response in Shank3 mutants than in wild-type control dogs. In addition, we found that Shank3 mutants showed compromised pupillary synchrony during dog-human interaction. These findings of altered pupil response in Shank3 mutant dogs recapitulate the altered sensory responses in ASD patients. Thus, this study demonstrates the validity and value of the pupilometer for dogs, and provides an effective paradigm for studying the underlying neural mechanisms of ASD and potentially other psychiatric disorders.

Visual perception of emotion cues in dogs: a critical review of methodologies

Comparative studies of human-dog cognition have grown exponentially since the 2000's, but the focus on how dogs look at us (as well as other dogs) as social partners is a more recent phenomenon despite its importance to human-dog interactions. Here, we briefly summarise the current state of research in visual perception of emotion cues in dogs and why this area is important; we then critically review its most commonly used methods, by discussing conceptual and methodological challenges and associated limitations in depth; finally, we suggest some possible solutions and recommend best practice for future research. Typically, most studies in this field have concentrated on facial emotional cues, with full body information rarely considered. There are many challenges in the way studies are conceptually designed (e.g., use of non-naturalistic stimuli) and the way researchers incorporate biases (e.g., anthropomorphism) into experimental designs, which may lead to problematic conclusions. However, technological and scientific advances offer the opportunity to gather much more valid, objective, and systematic data in this rapidly expanding field of study. Solving conceptual and methodological challenges in the field of emotion perception research in dogs will not only be beneficial in improving research in dog-human interactions, but also within the comparative psychology area, in which dogs are an important model species to study evolutionary processes.

Eye Tracking in Dogs: Achievements and Challenges

In this article, we review eye-tracking studies with dogs (Canis familiaris) with a threefold goal; we highlight the achievements in the field of canine perception and cognition using eye tracking, then discuss the challenges that arise in the application of a technology that has been developed in human psychophysics, and finally propose new avenues in dog eye-tracking research. For the first goal, we present studies that investigated dogs' perception of humans, mainly faces, but also hands, gaze, emotions, communicative signals, goal-directed movements, and social interactions, as well as the perception of animations representing possible and impossible physical processes and animacy cues. We then discuss the present challenges of eye tracking with dogs, like doubtful picture-object equivalence, extensive training, small sample sizes, difficult calibration, and artificial stimuli and settings. We suggest possible improvements and solutions for these problems in order to achieve better stimulus and data quality. Finally, we propose the use of dynamic stimuli, pupillometry, arrival time analyses, mobile eye tracking, and combinations with behavioral and neuroimaging methods to further advance canine research and open up new scientific fields in this highly dynamic branch of comparative cognition.