Optimal eye movement strategies in visual search

• Published in: Nature (London) Vol. 434; no. 7031; pp. 387 - 391
• Main Authors: Najemnik, Jiri, Geisler, Wilson S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.03.2005; Nature Publishing; Nature Publishing Group
• Subjects: Biological and medical sciences; Brain - physiology; Computer Simulation; Eye and associated structures. Visual pathways and centers. Vision; Eye Movements - physiology; Eyes & eyesight; Fixation, Ocular - physiology; Fundamental and applied biological sciences. Psychology; Humanities and Social Sciences; Humans; letter; Mammals; multidisciplinary; Photic Stimulation; Psychomotor Performance - physiology; Science; Science (multidisciplinary); Sensory perception; Vertebrates: nervous system and sense organs; Vision, Ocular - physiology; Visual task performance; Human; Rapid eye movement; Visual search; Vision; Strategy; Eye movement; Optimization
• ISSN: 0028-0836; 1476-4687; 1476-4687
• DOI: 10.1038/nature03390

Good looking, so refined Few activities are more important for survival than searching the local area with the eyes to find relevant objects: food, predators, potential mates, oncoming cars. Nonetheless, eye movements recorded during visual search often appear haphazard; it has even been suggested that gaze directions are selected randomly. A study in human subjects tasked to spot a target hidden in a cluttered background now shows that the process is far from random: human eye movements are very near to the mathematically determined optimal strategy. The model developed for this work can also be used to analyse search strategies in other species, and in the refinement of robotic vision systems. 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 locations 1 , 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 observer 3 , 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 scenes 7 . 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 fixations 8 , 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.