This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of this project is to identify mechanisms of motor learning in healthy adult subjects, with a view toward improving the rate and extent of learning in simple motor tasks such as reaching or drawing. We will make use of an influential experimental protocol developed by Shadmehr and Mussa-Ivaldi (1994), and now widely used in motor learning research (see review, Reinkensmeyer et al. 2004). Specifically, we will use a lightweight robotic device to apply small forces to the subject's hand, and then measure how the subject learns to move in the presence of those forces.
The specific aims are to: 1. Determine whether mechanical guidance can improve the ability of a subject to perform a novel movement. We will use the robot to mechanically guide the subject's hand along a desired path, then measure the ability of the subject to replicate the path without robotic guidance. In some cases, the robot will visually demonstrate the desired path to the subject by moving along it as the subject watches with his or her hands in her lap. The subject will then attempt to replicate the desired path. 2. Identify a computational model of motor adaptation. We will use the robot to mechanically perturb the subject's hand, and then quantify the ability of the subject to overcome the perturbation and move in their normal way. The robotic device will measure the path of the hand as the subject moves. We will use this data to identify a mathematical equation that describes that evolution of the movement error in response to the robot forces. We will then use the mathematical equation to design strategies for improving the rate or extent of adaptation in response to the perturbation. For example, we have recently shown for leg motion during walking that transiently increasing stepping errors can increase the rate of motor adaptation in response to a novel dynamic environment (Emken and Reinkensmeyer 2005). Understanding how healthy individuals learn to move in novel dynamic environments may ultimately improve strategies for motor rehabilitation following neurologic injuries such as stroke and traumatic brain injury.
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