The human motor-learning system rapidly optimizes brain networks into energy-efficient patterns to enhance motor execution in novel environments—a capability pivotal for advancing robotic adaptability in human-machine interaction (HMI). However, the specific energy-efficient neural patterns underlying this optimization remain unelucidated. Inspired by the self-organization and energy efficiency of biological neural networks, this study investigates brain network optimization mechanisms in low-sample repetitive motor skill acquisition. A high visual-motor coordination task (urban driving simulator) revealed consistent cognitive performance improvements that peaked with repeated practice. Dynamic high-order brain connectivity analysis identified three critical findings with direct relevance to HMI: first, brain connectivity undergoes sparsification, characterized by a transition from bilateral to unilateral parietal activation that forms an energy-saving neural pattern for motor control; second, network modularity is enhanced, which correlates with stabilized motor execution accuracy; third, the visual network exhibits progressive reduction in reliance on real-time visual input, which demonstrates an energy-efficient motor learning leveraging prior visual memory and holds critical value for lowering “perceptual load” in robotic systems. These results provide empirical evidence for brain network simplification during repetitive motor practice, and offer actionable guidance for designing energy-efficient, adaptive neural networks in robots navigating unfamiliar environments, thereby advancing HMI performance and robotic adaptability.
Li et al. (Thu,) studied this question.