Personalizing motor learning

本项目旨在通过为每个学习者定制练习时间表来提高运动技能学习。研究人员提出了一种新的算法，将产生实践时间表。时间表将取决于学习者的独特属性、来自其他学习者的数据以及对训练总量的可能限制。使用在线运动学习任务，研究人员将在从社区招募的成年人中测试该算法。在未来的应用中，该算法具有很强的潜力，以提高学习的体育，技术培训和手术技术培训。这项工作也与治疗中风、脊髓损伤、创伤性脑损伤和帕金森病等疾病的运动症状有关。拟议的研究将为从高中到博士的学生提供教育机会跨学科，如人工智能，脑科学和心理学。研究人员提出了一种新颖的、理论上合理的、自我改进的算法来个性化运动适应训练。该算法将选择训练的每日剂量和时间表，以最大限度地提高运动记忆动态模型预测的长期表现，考虑到学习者的独特特征，来自其他学习者的数据以及对总练习剂量和每日练习剂量的限制。研究人员将通过交叉验证比较具有不同记忆时间尺度的模型的预测能力。然后，研究人员将对大学生（18-30岁）进行培训，他们将在3天内学习在线运动适应任务，然后进行1个月的培训后保持测试。然后，研究人员将通过将在线任务部署到社区来测试个性化学习的有效性。性别、年龄、基线运动方差、遗传因素（BDNF、APOE基因）、一天中的时间和空间记忆协变量将被纳入模型中，以改善预测。由于算法是自我改进的，研究人员将比较每个新的30名参与者的子组与前一个子组在1个月培训后测试中的表现。此外，为了测试自适应调度相对于“一刀切”调度的功效，研究者将最后一组的表现与另一组匹配的参与者（n=30）的表现进行比较，这些参与者将接受三天的同等-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。

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.