Motor Skill Acquisition in a Virtual Gaming Environment

Abstract

There are many domains that still require use of complex manual control, despite the general shift in the field toward research on supervisory control. One of the problems in complex manual control is training; we currently lack models that can help guide training. The research reported here is part of an effort to fill that gap. In this study, we used the Neverball video game as a motor control task and used performance metrics from the game to measure learning. In addition, we collected motion data to determine what basic movements correlated with game performance. Subjects showed evidence of learning in almost all of the performance metrics, which will enable comparisons with the motion data. The ultimate goal is to use the motion data to identify basic movements that underlie successful performance to provide as feedback during training, and hopefully accelerate learning.