Modeling Human Visuomotor Adaptation with a Disturbance Observer Framework

In this project we propose an abstract discrete-time state space model of visuomotor adaptation based on the internal model principle. The proposed "DO Model" addresses key modeling requirements: modular architecture, physically relevant signals, parameters tied to atomic behaviors, and capacity for abstraction. The two main computational modules are a disturbance observer, a recently developed class of internal models, and a feedforward system that learns from the disturbance observer to improve feedforward motor commands.