title: Feedforward impedance control efficiently reduce motor variability.
abstract: Despite the existence of neural noise, which leads variability in motor commands, the central nervous system can effectively reduce movement variance at the end effector to meet task requirements. Although online correction based on feedback information is essential for reducing error, feedforward impedance control is another way to regulate motor variability. This Update Article reviews key studies examining the relation between task constraints and impedance control for human arm movement. When a smaller reaching target is given as a task constraint, flexor and extensor muscles are co-activated, and positional variance is decreased around the task constraint. Trial-by-trial muscle activations revealed no on-line feedback correction, indicating that humans are able to regulate their impedance in advance. These results demonstrate that not only on-line feedback correction, but also feedforward impedance control, helps reduce the motor variability caused by internal noise to realize dexterous movements of human arms. A computational model of movement planning considering the presence of signal-dependent noise provides a unifying framework that potentially accounts for optimizing impedance to maximize accuracy. A recently proposed learning algorism formulated as a V-shaped learning function explains how the central nervous system acquires impedance to optimize accuracy as well as stability and efficiency.
authors.affiliation: ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan. osu@atr.jp
authors.forename: Rieko
authors.initials: R
authors.lastname: Osu
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Morishige
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Miyamoto
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawato
mesh_terms: D001132:Arm / Q000502:physiology*; D002490:Central Nervous System / Q000502:physiology; D017097:Electric Impedance; D004576:Electromyography; D005246:Feedback; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000502:physiology; D011597:Psychomotor Performance; D013647:Task Performance and Analysis