Emergent togetherness through multilayer and high-order synchronization in generative dance neuro-motor systems

Collective improvisation in dance provides a rich natural laboratory for investigating emergent coordination in coupled neuro-motor systems. Here, we examine how training shapes spontaneous synchronization patterns across movement dynamics and brain activity during collaborative performance, with particular emphasis on the structure of higher-order interactions. Using a dual-recording protocol integrating 3D motion capture and hyperscanning EEG, participants engaged in free, interaction-driven, and rule-based improvisation tasks before and after a generative dance program grounded in cellular automata principles. Motor behavior was characterized through a time-resolved α-exponent derived from Movement Element Decomposition, capturing fluctuations in energetic strategies and the exploration of degrees of freedom. Synchronization events were quantified using Motif Synchronization for biomechanical data and multilayer Time-Varying Graphs for neural data. In order to investigate collective organization beyond pairwise coupling, biomechanical synchronization networks were further analyzed using simplicial complexes, allowing the characterization of interaction structures at multiple orders, including dyads, triads, and quartets. The results reveal that training modulates the distribution of interaction orders, with pronounced effects at the pairwise level. In parallel, neural data indicate increased inter-brain synchronization following training, particularly in frontal regions, suggesting enhanced alignment of internal cognitive and intentional processes. Together, these findings suggest that togetherness in collaborative improvisation emerges as a high-order dynamical property of social systems rather than as mere motor similarity. This study highlights the nonlinear and hierarchical nature of social coordination and offers a principled framework for modeling collective behavior in creative human systems.