Training strategies for learning a 3D trajectory with accuracy

The goal of this study was to evaluate different learning conditions for motor skill transfer. The study was divided into two experiments with the same task: learning a 3D trajectory with accuracy. The first experiment was focused on evaluating the efficiency of three feedback schemes for the target trajectory: visual, haptic and visual-haptic feedback. The second experiment was focused on analyzing the influence of decreasing the feedback during the training process. The results suggest that the best learning condition for learning a 3D trajectory with accuracy is to provide visual-haptic feedback, which facilitates the understanding of the dimension and orientation of each trajectory segment and solves any visual discrepancies that may exist. Furthermore, although continuous feedback can create dependences in users and impede the transfer of motor skills, feedback based on user request can also be dangerous since users can create a wrong mental representation that keep them from replicating the trajectory accurately. Therefore, when the performance of a task depends on references created during the training process, it seems appropriate for the system to provide automatic feedback based on user performance.