Optimal eye movement strategies in visual search Export

@article{JajemnikGeisler:OptimalEyeMovements, abstract = {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.}, author = {Najemnik, Jiri and Geisler, Wilson S.}, citeulike-article-id = {130384}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature03390}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature03390}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15772663}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15772663}, day = {17}, doi = {10.1038/nature03390}, issn = {0028-0836}, journal = {Nature}, keywords = {bayesian, eye\_movements}, month = {March}, number = {7031}, pages = {387--391}, posted-at = {2009-09-24 13:43:21}, priority = {2}, publisher = {Nature Publishing Group}, title = {Optimal eye movement strategies in visual search}, url = {http://dx.doi.org/10.1038/nature03390}, volume = {434}, year = {2005} }

TY - JOUR ID - JajemnikGeisler:OptimalEyeMovements L3 - citeulike-article-id:130384 N2 - 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. IS - 7031 JF - Nature SN - 0028-0836 EP - 391 TI - Optimal eye movement strategies in visual search PB - Nature Publishing Group VL - 434 SP - 387 KW - bayesian KW - eye_movements AU - Najemnik, Jiri AU - Geisler, Wilson PY - 2005/03/17/ UR - http://dx.doi.org/10.1038/nature03390 ER -