Modeling memory to enhance motor learning

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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.