Optimizing learning of a locomotor task: Amplifying errors as needed

Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom robotic stepper (MARCOS). Subjects were requested to actively coordinate their legs in a desired gait-like pattern in order to track a Lissajous figure presented on a visual display. Learning with three different training strategies was evaluated: (i) No perturbation: the robot follows the subjects´ movement without applying any perturbation, (ii) Error amplification: existing errors were amplified with repulsive forces proportional to errors, (iii) Noise disturbance: errors were evoked with a randomly-varying force disturbance. Results showed that training without perturbations was especially suitable for a subset of initially less-skilled subjects, while error amplification seemed to benefit more skilled subjects. Training with error amplification, however, limited transfer of learning. Random disturbing forces benefited learning and promoted transfer in all subjects, probably because it increased attention. These results suggest that learning a locomotor task can be optimized when errors are randomly evoked or amplified based on subjects´ initial skill level.