title: A gain-field encoding of limb position and velocity in the internal model of arm dynamics.
abstract: Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.
authors: Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. ehwang@bme.jhu.edu
authors: Eun Jung
authors: EJ
authors: Hwang
authors: Opher
authors: O
authors: Donchin
authors: Maurice A
authors: MA
authors: Smith
authors: Reza
authors: R
authors: Shadmehr
mesh_terms: D000328:Adult; D001132:Arm / Q000033:anatomy & histology* / Q000502:physiology; D001696:Biomechanical Phenomena; D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000502:physiology*; D005121:Extremities / Q000033:anatomy & histology* / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; D015233:Models, Statistical; D008962:Models, Theoretical; D009048:Motor Skills; D009068:Movement; D009474:Neurons / Q000378:metabolism; D011597:Psychomotor Performance; D012371:Robotics; D012984:Software