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Reaching hand movements tend to follow straight paths. Previous work has suggested that when visual feedback is perturbed such that straight hand motions are seen as curved motions, the motor system adapts to restore straight visual motion. We show that under a nonlinear visuomotor transformation, one that maps straight hand motions to high-curvature motions of a visual cursor, reaching movements do not converge with practice toward a straight path of either the hand or the cursor. Instead, hand trajectories converged to a repeatable and characteristic curved shape. We propose a new computational model in which the adapted trajectories are obtained by minimizing a cost function composed of two terms. The first term enforces hand-movement smoothness. The second term penalizes average visual aiming error, which is the instantaneous discrepancy between the direction of the hand movement and the direction of the vector that points from the cursor to the target. Our results are consistent with the model's predictions and demonstrate a persistent effect of the predicted feedback of direction errors despite the possibility of producing smoother hand motions by ignoring it.

Seeing versus Believing: Conflicting Immediate and Predicted Feedback Lead to Suboptimal Motor Performance