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.
The need for effective scheduling of motor tasks is ubiquitous in activities such as sports, music, professional skill development, and rehabilitation after brain injury, and is crucial in computerized training programs. The goal of learning is long-term retention and generalization to new tasks; current performance, however, is a poor predictor of long-term retention. The general hypothesis of this work 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 based on neuro-scientific computational models of the motor memory system. Intellectual merit: Our first focus was to determine the mechanisms of motor adaptations in humans, using a combined computational and behavioral approach. We have notably showed the positive effects of rewards on long-term retention of motor learning, as well as the dissociation between reward-based and error-based visuomotor adaptation. We have also studied the role of different practice schedules on memory formation and forgetting. We have notably showed how both interfering tasks and the passage of time reduced performance during training and enhanced retention by inducing trial-by-trial memory decay. Finally, we have also studied the neural mechanisms underlying motor learning; we notably showed that the slow component of motor adaptation was acquired in the cerebellum, with performance errors modifying synapses in the cerebellar Purkinje cells. Our second focus was to investigate methods for tailoring training schedules to individual learners. We have notably developed a model-based predictive method to determine optimal schedule in motor learning, that is, schedules that maximize retention Broader impacts: With this grant, we have examined scheduling and feedback conditions that maximize long-term retention of motor adaptation and skills. Such work has application in enhancing performance in sports and technical training, in both "traditional" and computer-based training. In addition, the funding has reinforced the theoretical bases for the new sub-discipline of computational neuro-rehabilitation, which has potential for large impact in health care by predicting recovery and optimizing motor therapy following brain injury.
