Abstract

SummaryTail wagging plays an important role in social interactions, e.g., dogs show asymmetrical tail wagging in response to different social stimuli. However, the effects of social cues on tail wagging and the intrinsic organization of wagging behavior remain largely unknown. Here, we developed a platform using a deep-learning-based motion-tracking technique to extract and analyze the movement trajectory of a dog’s tail tip during dog-human interactions. Individual dogs exhibited unique and stable wagging characteristics. We further found that tail wagging developed asymmetry toward the right side over three days of dog-human interactions, suggesting that it is a time-sensitive indicator of social familiarity. In addition, wagging appeared to follow an attractor-like dynamic process consisting of stable states and unstable, transitional states. Together, these results revealed sophisticated characteristics and organization of a dog’s tail-wagging behavior during interactions with humans, providing a useful paradigm for studying dogs’ social behaviors and the underlying neural mechanisms.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.