Haptic Force Feedback Enhances de Novo Learning of Arm Kinematics and Dynamics

The human motor system exhibits remarkable plasticity: not only can we master complex skills, but we can also learn to control artificial effectors. Here, we examine whether haptic force feedback from a simulated endpoint mass facilitates the de novo learning of novel kinematic and dynamic mappings. We investigate this using a virtual 2D planar arm controlled via a bimanual robotic manipulandum. Human participants moved two handles constrained to 1D channels, with handle positions specifying shoulder and elbow joint angles. Across two days, they performed center-out and out-back movements under two experimental conditions. On Day 1, one half of the participants practiced with a purely kinematic arm, whereas the other half received continuous haptic feedback from an endpoint mass. Although intrinsic hand movements were often initially sequential, participants rapidly adopted more coordinated control strategies, and trajectories became straighter with practice. Notably, the haptic-feedback group exhibited significantly greater improvements in virtual arm control. On Day 2, we introduced a velocity-dependent curl field to both groups. The curl field initially resulted in curved, loopy paths, but participants gradually compensated, consistent with internal-model formation. Prior exposure to haptic feedback conferred a superior capacity to adapt to the novel dynamics, evidenced by straighter trajectories and smaller directional biases throughout force-field exposure. These findings suggest haptic feedback enhances sensorimotor learning, possibly by engaging a distributed neural network of brain regions involved in motor skill acquisition and refinement.