This project focuses on coordination of eye and hand movements in carrying out simple tasks. Using gaze-contingent displays, we simulate the kinds of retinal damage that are associated with glaucoma, retinitis pigmentosa and age-related macular degeneration, evaluate how damage affects eye-hand coordination and measure how quickly subjects learn to compensate. In three series of experiments, we track eye movements and hand movements and their interaction. Bayesian decision theory provides a very natural way to model and better understand how humans plan movements. The first goal of this research is to extend existing Bayesian decision-theoretic models of movement planning to include eye and hand movements and their interactions. The result will be a predictive model of human planning of movement. A second goal is to better understand how the visuo-motor system learns to compensate for damage due to retinal disease or injury and how to speed such compensation.