title: Motor adaptation as an optimal combination of computational strategies.
abstract: To efficiently and accurately manipulate objects, the nervous system must adjust motor commands based on experience. Four major adaptive strategies that could help achieve this goal are: internal model formation of the environmental dynamics, minimizing force, trajectory planning, and selectively stiffening the arm. We measured motor adaptation to a robotic force field with and without a large background force requirement. We then developed a computational model of motor adaptation that allowed the relative contribution of the four strategies to be estimated. Motor adaptation was best modeled as a blend of strategies, with internal model formation playing a greater role when forces were smaller and predictable; impedance control had a higher priority when forces were smaller and unpredictable; force minimization was more important when forces were larger; and trajectory planning was involved in both large and small background force conditions. These results are consistent with the viewpoint that the nervous system effectively seeks to minimize a cost-function containing force, stiffness, and position error terms.
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA.
authors.forename: J
authors.initials: J
authors.lastname: Liu
authors.forename: D
authors.initials: D
authors.lastname: Reinkensmeyer