Characterizing Individual Differences in a Dynamic Stabilization Task Using Machine Learning.

INTRODUCTION: Being able to identify individual differences in skilled motor learning during disorienting conditions is important for spaceflight, military aviation, and rehabilitation. METHODS: Blindfolded subjects (N = 34) were strapped into a device that behaved like an inverted pendulum in the horizontal roll plane and were instructed to use a joystick to stabilize themselves across two experimental sessions on consecutive days. Subjects could not use gravitational cues to determine their angular position and many soon became spatially disoriented. RESULTS: Most demonstrated minimal learning, poor performance, and a characteristic pattern of positional drifting during horizontal roll plane balancing. To understand the wide range of individual differences observed, we used a Bayesian Gaussian Mixture method to cluster subjects into three statistically distinct groups that represent Proficient, Somewhat Proficient, and Not Proficient performance. We found that subjects in the Not Proficient group exhibited a suboptimal strategy of using very stereotyped large magnitude joystick deflections. We also used a Gaussian Naive Bayes method to create predictive classifiers. As early as the second block of experimentation (out of ten), we could predict a subject's final group with 80% accuracy. DISCUSSION: Our findings indicate that machine learning can help predict individual performance and learning in a disorienting dynamic stabilization task and identify suboptimal strategies in Not Proficient subjects, which could lead to personalized and more effective training programs.