The National Institute of Neurological Disorders and Stroke (NINDS) awarded Yale University a $440,610 Project Grant under the Extramural Research Programs in the Neurosciences and Neurological Disorders (CFDA 93.853) to investigate fundamental predictive computations in motor learning for upper-limb and speech adaptation. The research aims to elucidate the neural signals required for error-based motor learning, testing the hypothesis that motor planning and sensory prediction error signals are necessary and sufficient for motor learning, even in the absence of physical movement. Through behavioral studies, electromyography, and functional neuroimaging, the project will explore this framework in both upper-limb adaptation and speech adaptation, providing a generalization test of the predictive processing model of motor learning. The results are expected to yield mechanistic and clinical insights to inform future rehabilitation strategies, brain-computer interface technology, and our understanding of human predictive processing.
Generated 3/4/25, 5:29 AM