Infomax models of oculomotor control

From a Bayesian point of view, learning is simply the process of making inferences about the world based on incoming data. The efficiency of this learning is determined by the quality of the information provided by the sensors. Thus, a critical part of learning is the existence of a sensory-motor system designed to maximize the information required to achieve goals. Here we show that a wide range of primate eye movement phenomena can be elegantly explained from the point of view of infomax control. The proposed approach describes the velocity profiles of saccadic eye movements as well as previously existing models. In addition, the infomax approach explains phenomena that are beyond the scope of previous models: non-saccadic eye movements, and the difference in end point and velocity profiles observed in saccade-to-target and reach-to-target tasks. The results suggest that the occulomotor control system evolved to be a very efficient real time learning machine.