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    PhD/MSTP Students

    • 1688
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    19992016

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    Our lab investigates computations made by people as we plan, control, and learn movements. Human subjects in our lab make natural reaching movements. To complete these tasks, people must transform simple task requirements into a full movement. We investigate the constraints people use to choose appropriate muscle, arm, and hand trajectories. Other subjects in our lab train to make reaching movements in novel environments. These environments challenge the adaptive human sensory and motor systems by providing novel force feedback, using a robotic arm, or by providing altered sensory feedback. The performance of subjects on these tasks is compared to computational simulations. These simulations model the mechanics of the robotic arm, the biomechanics of the human arm, and the neuronal network underlying behavior. By comparing human and model behavior, we aim to understand: How do we generate movement plans? If our movement planning is in error, how do we recover mid-movement to best complete a task? How do we abstract information from erroneous movements to improve subsequent performance? These studies will identify the transient and steady state properties of the computational and neural processes underlying human motor behavior.

    Available to Mentor:

    • PhD/MSTP Students

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