The human motor system can learn to control novel effectors, but the contribution of task-relevant haptic dynamics to de novo learning remains unclear. Using a bimanual robotic interface, participants learned over two days to control the shoulder and elbow angles of a virtual arm in order to achieve accurate endpoint movements via constrained handle motions. On Day 1, one group practiced a purely kinematic mapping, whereas another group received continuous haptic feedback generated by an endpoint mass. With practice, movements shifted from sequential to more coordinated control and trajectories became straighter, with reduced directional deviation during target-directed endpoint movements, particularly in the haptic-feedback group. On Day 2, both groups learned to compensate for a velocity-dependent force field. Trajectories were initially curved but straightened with practice, and washout produced after-effects. Training in the presence of task-relevant haptic dynamics was associated with more complete error reduction during force-field exposure, while maintaining robust after-effects. Exponential modeling provided no evidence for a difference in learning rate between groups but was consistent with a lower residual (asymptotic) error in the haptic-feedback condition. These benefits therefore reflected a difference in final predictive compensation rather than in the speed of adaptation. Together, these results suggest that performance in the presence of task-relevant haptic dynamics was associated with more complete predictive compensation when adapting to novel dynamics, without evidence of faster adaptation.
Howard et al. (Wed,) studied this question.