Task-Relevant Haptic Feedback Enhances de novo Acquisition of Novel Arm Control

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. Prior haptic training led to more complete error reduction while maintaining robust after-effects. These benefits reflected a higher asymptotic level of predictive compensation in the haptic feedback condition, rather than an increased learning rate. Together, these results indicate that learning with task-relevant haptic dynamics shapes control policies for novel kinematic and dynamic relationships, enhancing subsequent sensorimotor adaptation by improving the quality and completeness of predictive control rather than simply accelerating learning.