Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a Difficult Motor Task.
One can use a number of techniques (e.g., from videotaping to computer enhancement of the environment) to augment the feedback that a subject usually receives during training on a motor task. Although some forms of augmented feedback have been shown to enhance performance on isolated isometric tasks during training, when the feedback has been removed subjects have sometimes not been able to perform as well in the "real-world" task as controls. Indeed, for realistic, nonisometric motor tasks, improved skill acquisition because of augmented feedback has not been demonstrated. In the present experiments, subjects (Experiment 1, N = 42; Experiment 2, N = 21) performed with a system that was designed for teaching a difficult multijoint movement in a table tennis environment. The system was a fairly realistic computer animation of the environment and included paddles for the teacher and subject, as well as a virtual ball. Each subject attempted to learn a difficult shot by matching the pattern of movements of the expert teacher. Augmented feedback focused the attention of the subject on a minimum set of movement details that were most relevant to the task; feedback was presented in a form that required the least perceptual processing. Effectiveness of training was determined by measuring their performance in the real task. Subjects who received the virtual environment training performed significantly better than subjects who received a comparable amount of real-task practice or coaching. Kinematic analysis indicated that practice with the expert's trajectory served as a basis for performance on the real-world task and that the movements executed after training were subject-specific modifications of the expert's trajectory. Practice with this trajectory alone was not sufficient for transfer to the real task, however: When a critical component of the virtual environment was removed, subjects showed no transfer to the real task.
Todorov
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E
a Massachusetts Institute of Technology.
Shadmehr
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R
b Johns Hopkins University.
Bizzi
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E
a Massachusetts Institute of Technology.