Distinct optimization of motor control and learning: Self-paced motor learning achieves different motor patterns from overtrained

Self-paced movement, resulting from extensive training and optimization, serves as an ideal model for investigating internal objectives without external constraints within the sensorimotor system. Previous studies have shown that the self-paced movement is consistent within individuals but inconsistent across individuals, highlighting a robust, unique optimization in motor control. Although motor learning is similarly an optimization process, it is unknown whether the internal objectives are shared with motor control and learning, i.e., whether it replicates the motor patterns identified by self-paced motor control. As the optimality principles typically address redundancy, a human behavioral experiment was designed to equate movement distance for motor control and learning, comparing redundant motion parameters, movement velocity, and duration, through detailed analysis that accommodates their nonlinear interaction and significant inter-individual baseline differences. Participants, instructed to use their preferred velocities and duration, exhibited minimal intra-individual variability in motion parameters. Notably, visuomotor shift perturbations exclusively affected velocity, while changes in target distances affected both velocity and duration. These findings suggest that the sensorimotor system employs distinct optimization mechanisms for motor control and learning to resolve redundancy.