2026-08-31 アリゾナ大学
Using a coding system, the researchers found that one-tenth of the total time that pet owners spend talking is with their pets, and that no two owners sound quite alike.
＜関連情報＞
- https://news.arizona.edu/news/new-u-tool-decodes-pet-talk-and-may-help-explain-how-pets-affect-our-well-being
- https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2026.1881870/full
異種間会話：日常生活における人とペットの会話を捉えるためのAURAL-Petコーディングシステムの開発 Cross-species conversations: the development of the AURAL-Pet coding system to capture how people talk to pets in daily life
Dara S. Jonkoski,Nicole M. Lorig,Molly C. Delzio,Alyssa Klensin,Abigail Marsters,Huashi Li,Amanda M. Bernal,Matthias R. Mehl,Kerri E. Rodriguez,Emily E. Bray
Frontiers in Psychiatry Published:04 August 2026
DOI:https://doi.org/10.3389/fpsyt.2026.1881870
Abstract
While pets are a well-established fixture in many households, how owners perceive their pets’ roles can range from distant housemates to casual companions to trusted confidantes. At the same time, findings relating to the impact pets have on human health and wellbeing are mixed. A potential explanation for these disparate effects is the naturally occurring variation in daily human-pet interactions and relationship quality. However, there is a lack of methodological approaches that can accurately quantify naturalistic interactions between people and their pets with limited bias. To address this gap, we developed the Auditory-based Understanding of Relational Animal-directed Language (AURAL-Pet) coding system to apply to ambient sound clips of human-pet interactions as captured by the Electronically Activated Recorder (EAR). By consulting existing literature, subject matter experts, and an archival database of EAR audio clips, we used an iterative process to develop and refine the coding system. In the final version, we retained 30 variables, 27 binary and 3 qualitative, which extract aspects of pet-focused activities, as well as the quality and content of human speech directed towards pets. We found that 22 out of 23 original AURAL-Pet binary variables had acceptable inter-coder agreement (ICC ≥.70). We then applied AURAL-Pet to four archival EAR studies, totaling N = 5,072 ambient audio clips containing evidence of pet presence across N = 240 participants, to better understand how frequently participants demonstrated different human-pet interactions across gender- and age-diverse samples. We found that individual participants’ interactions with pets varied extensively across all variables, and that certain variables were more heterogeneous across genders and ages than others. In summary, this coding tool provides a novel, reliable method for quantifying daily human-pet interactions across diverse populations. Future work applying AURAL-Pet to naturalistic audio clips would be well-positioned to explore how differences in human-animal interactions may impact both human and animal health and wellbeing.
