Computational investigation of the social function of domestic cat facial signals.

Abstract

There is growing interest in the facial signals of domestic cats. Domestication may have shifted feline social dynamics towards a greater emphasis on facial signals that promote affiliative bonding. Most studies have focused on cat facial signals during human interactions or in response to pain. Research on intraspecific facial communication in cats has predominantly examined non-affiliative social interactions. A recent study by Scott and Florkiewicz1 demonstrated significant differences between cats' facial signals during affiliative and non-affiliative intraspecific interactions. This follow-up study applies computational approaches to make two main contributions. First, we develop a machine learning classifier for affiliative/non-affiliative interactions based on manual CatFACS codings and automatically detected facial landmarks, reaching above 77% in CatFACS codings and 68% in landmarks by integrating a temporal dimension. Secondly, we introduce novel measures for rapid facial mimicry based on CatFACS coding. Our analysis suggests that domestic cats exhibit more rapid facial mimicry in affiliative contexts than non-affiliative ones, which is consistent with the proposed function of mimicry. Moreover, we found that ear movements (such as EAD103 and EAD104) are highly prone to rapid facial mimicry. Our research introduces new possibilities for analyzing cat facial signals and exploring shared moods with innovative AI-based approaches.