Learned dynamics of reaching movements generalize from dominant to nondominant arm Academic Article uri icon

abstract

  • Accurate performance of reaching movements depends on adaptable neural circuitry that learns to predict forces and compensate for limb dynamics. In earlier experiments, we quantified generalization from training at one arm position to another position. The generalization patterns suggested that neural elements learning to predict forces coded a limb's state in an intrinsic, muscle-like coordinate system. Here, we test the sensitivity of these elements to the other arm by quantifying inter-arm generalization. We considered two possible coordinate systems: an intrinsic (joint) representation should generalize with mirror symmetry reflecting the joint's symmetry and an extrinsic representation should preserve the task's structure in extrinsic coordinates. Both coordinate systems of generalization were compared with a naive control group. We tested transfer in right-handed subjects both from dominant to nondominant arm (D→ND) and vice versa (ND→D). This led to a 2 × 3 experimental design matrix: transfer dir...

publication date

  • January 1, 2003