Bridging motor invariants and individuality: large-scale quantification of motor styles across action domains

Human movement is governed by universal kinematic invariants, yet every person also ‘moves in their own way’. How these individual motor styles persist or reorganize across different motor contexts is unknown. Here we analyzed upper-limb movements from 90 participants performing six tasks spanning transitive, intransitive, and pointing actions domains. Using a structured computational framework, we show that individuality is not random fluctuation but a coherent property of motor control: it is stable within tasks and reshapes when control architectures (motor complexity, higher-order action planning) change. Cross-task analyses revealed a domain-specific organization of individuality, with strong coherence within transitive and intransitive domains. Additional hierarchical clustering uncovered latent motor phenotypes, revealing a multilayered architecture of human motor individuality: a system in which shared kinematic laws shape how humans can move, and personal control schemes shape how each person does move.