An efficient technique for revealing visual search strategies with classification images

We propose a novel variant of the classification image paradigm that allows us to rapidly reveal strategies used by observers in visual search tasks. We make use of eye tracking, 1/f noise, and a grid-like stimulus ensemble and also introduce a new classification taxonomy that distinguishes between foveal and peripheral processes. We tested our method for 3 human observers and two simple shapes used as search targets. The classification images obtained show the efficacy of the proposed method by revealing the features used by the observers in as few as 200 trials. Using two control experiments, we evaluated the use of naturalistic 1/f noise with classification images, in comparison with the more commonly used white noise, and compared the performance of our technique with that of an earlier approach without a stimulus grid.