Modeling memory to enhance motor learning

模拟记忆以增强运动学习

• 批准号: 1031899
• 负责人: Nicolas Schweighofer
• 金额: $ 34.45万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1031899&HistoricalAwards=false
• 关键词: Modeling; memory; enhance; motor; learning

This proposal takes an innovative stand in proposing that computational neuroscience can guide the design of effective and motivating adaptive training schedules for motor tasks. Although the goals of learning are generalization and long-term retention, current performance is a poor predictor of these learning goals. In this proposal, the general hypothesis is that adaptive scheduling of multiple motor tasks, based on long-term memory predictions, can enhance learning and that these long-term predictions are most effective when derived from neurally-based computational models of the motor memory system. The two specific research objectives of the proposed work are 1) to determine the mechanisms of multiple motor adaptation in humans, using a combined computational and behavioral approach, and 2) to investigate methods for tailoring training schedules to individual learners using multiple motor adaptation tasks.The proposed research is in line with two of the 14 grand challenges for the 21st century, identified by the U.S. National Academy of Engineering (NAE): 'reverse-engineering the brain' and 'advancing personalized learning.' The work proposed considers these challenges as related and that 'advancing personalized learning' must be based on an understanding of the learning and motivational systems of the brain. Although there have been a few attempts to generate learning programs along these lines, this type of research is still in its infancy and is mostly based on descriptive models of learning and memory. Here, the PI will reverse-engineer the motor memory system with computational models that are both neurally and behaviorally valid and relevant. This has the potential to be useful in a large number of applications, including rehabilitation of movement-impaired patients (e.g., stroke patients), sport and exercise education, dance instruction, and special needs education.

这项建议采取了创新的立场，提出计算神经科学可以指导设计有效的和激励性的自适应训练计划的运动任务。 虽然学习的目标是概括和长期保持，目前的表现是一个穷人的预测这些学习目标。在这个提议中，一般的假设是，基于长期记忆预测的多个运动任务的自适应调度可以增强学习，并且这些长期预测在来自运动记忆系统的基于神经的计算模型时是最有效的。拟议工作的两个具体研究目标是：1）使用计算和行为相结合的方法确定人类多种运动适应的机制，2）研究使用多种运动适应任务为个体学习者定制训练时间表的方法。拟议的研究符合21世纪世纪14大挑战中的两个，美国国家工程院（NAE）指出：“反向工程大脑”和“推进个性化学习”。这项工作认为这些挑战是相关的，“推进个性化学习”必须基于对大脑学习和动机系统的理解。虽然已经有一些尝试产生学习程序沿着这些路线，这种类型的研究仍然处于起步阶段，主要是基于学习和记忆的描述性模型。在这里，PI将使用神经和行为上有效且相关的计算模型对运动记忆系统进行逆向工程。这具有在大量应用中有用的潜力，包括运动障碍患者的康复（例如，中风患者）、体育和锻炼教育、舞蹈指导和特殊需要教育。