Personalizing motor learning

University Of Southern California, Los Angeles CA

Investigators

Abstract

This project aims to improve motor learning by customizing the practice schedule for each learner. The investigators propose a novel algorithm that will generate practice schedules. The schedules will depend on the learner’s unique attributes, data from other learners, and the possible limits on the total amount of training. Using an online motor learning task, the investigators will test the algorithm with adults across the lifespan recruited from the community. In future applications, the algorithm has the strong potential to improve learning in sports, technical training, and surgical technique training. This work is also relevant for treating motor symptoms in conditions such as stroke, spinal cord injury, traumatic brain injury, and Parkinson’s disease. The proposed research will provide educational opportunities for students from high school to Ph.D. across disciplines such as artificial intelligence, brain science, and psychology. The investigators propose a novel, theoretically sound, and self-improving algorithm to personalize motor adaptation training. The algorithm will select the daily dose and schedule of training that maximizes the long-term performance predicted by a dynamical model of motor memory, given the learner’s unique characteristics, data from other learners, and constraints on both total and daily doses of practice. The investigators will compare the predictive abilities of models with different memory time scales via cross-validation. The investigators will then pilot the training algorithm with college students (ages 18-30) who will learn an online motor adaptation task over 3 days, followed by a 1-month post-training retention test. Then, the investigators will test the efficacy of personalized learning by deploying the online task to the community. Sex, age, baseline movement variance, genetic factors (BDNF, APOE genes), time of day, and spatial memory covariates will be incorporated into the model to improve predictions. Because the algorithm is self-improving, the investigators will compare the performance in the 1-month post-training test of each new sub-group of 30 participants to that of the preceding sub-group. Furthermore, to test the efficacy of the adaptive schedule relative to a “one-size-fits-all” schedule, the investigators will compare the performance of the last group to that of an additional sub-group of matched participants (n=30) who will receive three days of equally-dosed practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.