Adaptive gaze control in natural environments.

The sequential acquisition of visual information from scenes is a fundamental component of natural visually guided behavior. However, little is known about the control mechanisms responsible for the eye movement sequences that are executed in the service of such behavior. Theoretical attempts to explain gaze patterns have almost exclusively concerned two-dimensional displays that do not accurately reflect the demands of natural behavior in dynamic environments or the importance of the observer's behavioral goals. A difficult problem for all models of gaze control, intrinsic to selective perceptual systems, is how to detect important but unexpected stimuli without consuming excessive computational resources. We show, in a real walking environment, that human gaze patterns are remarkably sensitive to the probabilistic structure of the environment, suggesting that observers handle the uncertainty of the natural world by proactively allocating gaze on the basis of learned statistical structure. This is consistent with the role of reward in the oculomotor neural circuitry and supports a reinforcement learning approach to understanding gaze control in natural environments.