A team led by Cat Hobaiter at the University of St Andrews will explore the communicative tools that wild chimpanzees use to navigate social conflict. The research will examine the specific gesture units and their meanings in agonistic interactions, as well as how individuals time and align their communication during conflict. It will also explore a potential mechanism in human conflict resolution: physiological synchronisation to rhythmic stimuli, such as shared music or movement.

Understanding the mechanisms that underpin human societies is complicated by rich cultural variation. But given our close evolutionary connections, our ability to understand the mechanisms that divide or unite individuals and communities in other apes allows us to understand the roots of these same capacities in humans. The team’s promising pilot data suggests that chimpanzees effectively use communication to manage tension and expand their social groups, offering tools that may be relevant to human communities at a global scale today.

Two hypotheses will be tested: that chimpanzee gestures provide an important tool in de-escalating aggression and re-engaging communication, and that chimpanzees in super-groups adjust communication to mitigate competition and maintain community unity.

To do so, the team plans to: 

• Investigate how chimpanzees use gestures to de-escalate aggression and re-engage communication, leveraging a large dataset to analyze variations in gesture usage across social hierarchies.
• Examine the role of temporal patterns and social synchrony in chimpanzee signals, comparing their gestural and rhythmic communication with pre-verbal children.
• Focus on physiological alignment through rhythmic activities in human communities, exploring how shared rhythms mediate social realignment.

The research will draw on longitudinal datasets from chimpanzee communities in Uganda, Guinea, and Micronesia, supplemented by new fieldwork. It will integrate behavioral coding, acoustic and rhythmic analysis, heart-rate monitoring, and machine learning tools, including acoustic cameras, for high-resolution study of gestures, synchrony, and physiological alignment.