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Human and Optimal Eye Movement Strategies in Visual Search

Dr. Wilson Geisler

Monday, February 21, 2005, 02:00pm - 03:00pm

The University of Texas at Austin, Center for Perceptual Systems

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 To perform visual search, humans, like many other mammals, encode a large field of view with a visual system having variable spatial resolution, and then use high-speed eye movements to direct the highest resolution region, the fovea, at potential target locations in the environment.  Visual search is a ubiquitous task where good performance is essential for survival, and hence mammals may have evolved very efficient strategies for selecting fixation locations during visual search.  Thus, my students and I asked two related questions.  What are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment?  Do humans employ optimal eye movement strategies while searching?  To answer these questions, we derived the Bayesian ideal visual searcher for the case where a known target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes.  We also characterized the variable spatial resolution of the human visual system by measuring the "visibility" (detectability) of the targets across the retina, and then constrained the ideal searcher with the same target visibility maps.  Finally, we compared human and ideal performance in the same search task.  We find that many qualitative properties of human fixation patterns are consistent with an optimal eye movement strategy.  Remarkably, we also find that humans achieve nearly optimal search performance, even though it is well known that humans do not integrate information perfectly across fixations.  By analyzing the ideal searcher we show that, in fact, there is only a small benefit from integrating information perfectly across fixations­much more important is efficient parallel processing of the information on each fixation.   It seems that evolution has exploited this fact to achieve very efficient visual search and eye movement strategies with minimal neural resources devoted to memory and information integration.

Dr. Wilson Geisler