Engineering Commons^™

A Reinforcement-Learning Framework For Interpreting Trial-By-Trial Motor Adaptation To Novel Haptic Environments, Ranjan Patrick Khan

McKelvey School of Engineering Theses & Dissertations

Motor adaptation is often considered to occur under the influence of sensory signals, which is usually readily available for humans performing most motor tasks. However, humans can also use reward or other qualitative feedback to reinforce previous actions and perform adaptation. In these experiments, we introduce reward feedback to a traditional motor adaptation experiment: reach adaptation to a velocity-dependent force field. Drawing from the literature of computer science and machine learning, we use a reinforcement-learning framework to interpret the pattern of force generation and reward-prediction errors and observe the effects of concurrent and isolated reward and sensory feedback.

It is …