Eye Movements for Reward Maximization

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Nathan Sprague, Dana Ballard


University of Rochester Rochester, NY 14627 dana@cs.rochester.edu

Recent eye tracking studies in natural tasks suggest that there is a tight link between eye movements and goal directed motor actions. However, most existing models of human eye movements provide a bottom up ac- count that relates visual attention to attributes of the visual scene. The purpose of this paper is to introduce a new model of human eye move- ments that directly ties eye movements to the ongoing demands of be- havior. The basic idea is that eye movements serve to reduce uncertainty about environmental variables that are task relevant. A value is assigned to an eye movement by estimating the expected cost of the uncertainty that will result if the movement is not made. If there are several candidate eye movements, the one with the highest expected value is chosen. The model is illustrated using a humanoid graphic figure that navigates on a sidewalk in a virtual urban environment. Simulations show our protocol is superior to a simple round robin scheduling mechanism.