Higher social tolerance is associated with more complex facial behavior in macaques

Facial expressivity in dominant macaques is linked to group cohesion

Social living affords primates (including humans) many benefits. Communication has been proposed to be the key mechanism used to bond social connections, which could explain why primates have evolved such expressive faces. We assessed whether the facial expressivity of the dominant male (quantified from the coding of anatomically based facial movement) was related to social network properties (based on social proximity and grooming) in nine groups of captive rhesus macaques (Macaca mulatta) housed in uniform physical and social environments. More facially expressive dominant male macaques were more socially connected and had more cohesive social groups. These findings show that inter-individual differences in facial expressivity are related to differential social outcomes at both an individual and group level. More expressive individuals occupy more beneficial social positions, which could help explain the selection for complex facial communication in primates.

Wild and captive immature orangutans differ in their non-vocal communication with others, but not with their mothers

Abstract

In many group-living species, individuals are required to flexibly modify their communicative behaviour in response to current social challenges. To unravel whether sociality and communication systems co-evolve, research efforts have often targeted the links between social organisation and communicative repertoires. However, it is still unclear which social or interactional factors directly predict communicative complexity. To address this issue, we studied wild and zoo-housed immature orangutans of two species to assess the impact of the socio-ecological setting on the production of non-vocal signal repertoires. Specifically, we compared repertoire size, dyadic repertoire similarity, and number of social goals (i.e. observer's estimate of the signaller's intended interaction outcome) for communicative interactions with mothers versus other conspecifics, controlling for critical individual and environmental factors. In this small sample of immature orangutans, wild-captive contrasts were statistically significant only for other-directed repertoires, but not for mother-directed repertoires, and not for the number of social goals that immatures communicated towards. While the repertoires of individuals living in the same research setting were more similar than those living in contrasting settings, this difference was most pronounced for other-directed repertoires of the less socially tolerant orangutan species. These results suggest that the boosted interactional opportunities in captivity rather than mere differences in environmental affordances or communicative needs drive the wild-captive contrast in orangutan communicative repertoires. Overall, this fine-grained analysis of repertoires further underscores that not only a species' social organisation but also the targeted audience may have a profound impact on communicative behaviour.

Significance statement

Navigating a dynamic social environment often requires flexible signal use. While it has repeatedly been shown that the social organisation and structure of species predict the complexity of their communication systems, the mechanisms underlying these relationships are largely unknown. Because targeted studies to assess this issue in great apes are difficult, we take an alternative approach here: we compare the same species living in the wild and in artificial habitats in captivity. This contrast allows a direct test of how repertoires respond to the relevant difference in socio-ecological conditions. Our results show that the diversity of interaction partners (i.e. social opportunities), but not the diversity of social goals (i.e. possible interaction outcomes) or the broader physical opportunities (i.e. safe ground use), predict the size and consistency of wild and captive signalling repertoires.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00265-023-03426-3.

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models

This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.