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.

Automated summary

Comparative studies on human-dog cognition have been rapidly expanding since the 2000s. The focus on how dogs perceive us as social partners is relatively new and important, especially in terms of human-dog interactions. This article summarizes the current state of research on dogs' visual perception of emotion cues, discusses the challenges and limitations in methodology and concept, and suggests possible solutions for future research. The conceptual design of studies and the incorporation of biases in experimental designs are key challenges, but advances in technology and science provide opportunities to gather more valid and objective data in this field.