title: Coordinate Representations for Interference Reduction in Motor Learning.
abstract: When opposing force fields are presented alternately or randomly across trials for identical reaching movements, subjects learn neither force field, a behavior termed 'interference'. Studies have shown that a small difference in the endpoint posture of the limb reduces this interference. However, any difference in the limb's endpoint location typically changes the hand position, joint angles and the hand orientation making it ambiguous as to which of these changes underlies the ability to learn dynamics that normally interfere. Here we examine the extent to which each of these three possible coordinate systems--Cartesian hand position, shoulder and elbow joint angles, or hand orientation--underlies the reduction in interference. Subjects performed goal-directed reaching movements in five different limb configurations designed so that different pairs of these configurations involved a change in only one coordinate system. By specifically assigning clockwise and counter-clockwise force fields to the configurations we could create three different conditions in which the direction of the force field could only be uniquely distinguished in one of the three coordinate systems. We examined the ability to learn the two fields based on each of the coordinate systems. The largest reduction of interference was observed when the field direction was linked to the hand orientation with smaller reductions in the other two conditions. This result demonstrates that the strongest reduction in interference occurred with changes in the hand orientation, suggesting that hand orientation may have a privileged role in reducing motor interference for changes in the endpoint posture of the limb.
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Sang-Hoon
authors.initials: SH
authors.lastname: Yeo
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
authors.forename: David W
authors.initials: DW
authors.lastname: Franklin
mesh_terms: D000328:Adult; D005246:Feedback; D005260:Female; D006225:Hand / Q000502:physiology; D006801:Humans; D007858:Learning; D008297:Male; D009068:Movement; D011597:Psychomotor Performance