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Optimal eye movement strategies in visual search


To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations1,2. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two 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, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer3,4,5,6 for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes7. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations8,9,10. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations—much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.

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The authors declare that they have no competing financial interests.


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We thank R.F. Murray for helpful discussions, and J. Perry, L. Stern and C. Creeger for technical assistance. This work was supported by the National Eye Institute, NIH.

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Competing interests

The authors declare that they have no competing financial interests.

Correspondence to Wilson S. Geisler.

Supplementary information

Supplementary Equations

This document contains a derivation of the ideal searcher for the case of dynamic (temporally uncorrelated) external and internal noise, and describes the ideal searcher for the case of static (temporally correlated) external noise and dynamic (temporally uncorrelated) internal noise. (PDF 181 kb)

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Further reading

Figure 1: Measurement of the visibility maps.
Figure 2: Representation of the visibility maps.
Figure 3: Ideal searcher.
Figure 4: Human versus ideal performance.


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