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Latest World News Update > Blog > Health > Learning about social interaction by studying dancing – World News Network
Health

Learning about social interaction by studying dancing – World News Network

By worldnewsnetwork Last updated: April 14, 2025 2 Min Read
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Washington DC [US], April 14 (ANI): Dancing fluidly with someone requires social coordination. This skill requires coordinating movements with others while also processing dynamic sensory input, such as sounds and visuals.
Felix Bigand and Giacomo Novembre from the Italian Institute of Technology in Rome, together with others, report on how the brain drives social coordination during dance.
The researchers recruited pairs of inexperienced dancers and recorded their brain activity, whole-body movements, and muscle activity as they danced to the same or different songs. The researchers also manipulated whether dancers could or could not see each other. These methods unveiled distinct neural signals for music processing, self-generated movements, movements generated by following a partner, and social coordination.
Neural signals for social coordination that enabled synchronized movements between people occurred only when dancers were moving to the same song and could see each other.
Bigand said, “What was perhaps most peculiar was we found that out of the 15 different movements we recorded, the brain was most sensitive to bouncing or flexing of the knees [during social coordination]. This was strange because bouncing had relatively weak amplitudes (or strength) compared to most of the other movements. For the brain to respond more to a weaker movement, like bounce, suggests it has a unique role in social coordination.”
According to the authors, this work advances our understanding of social interaction beyond dancing because it sheds light on how the brain supports socially engaging activities while integrating dynamic sensory information. Bigand also emphasizes that the methods used to unravel distinct neural signals for different kinds of sensory information processing may improve the applicability of future preclinical work to reality. (ANI)

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