Point-of-gaze analysis reveals visual search strategies

Seemingly complex tasks like visual search can be analyzed using a cognition-free, bottom-up framework. We sought to reveal strategies used by observers in visual search tasks using accurate eye tracking and image analysis at point of gaze. Observers were instructed to search for simple geometric targets embedded in 1/f noise. By analyzing the stimulus at the point of gaze using the classification image (CI) paradigm, we discovered CI templates that indeed resembled the target. No such structure emerged for a random-searcher. We demonstrate, qualitatively and quantitatively, that these CI templates are useful in predicting stimulus regions that draw human fixations in search tasks. Filtering a 1/f noise stimulus with a CI results in a ‘fixation prediction map’. A qualitative evaluation of the prediction was obtained by overlaying k-means clusters of observers’ fixations on the prediction map. The fixations clustered around the local maxima in the prediction map. To obtain a quantitative comparison, we computed the Kullback-Leibler distance between the recorded fixations and the prediction. Using random-searcher CIs in Monte Carlo simulations, a distribution of this distance was obtained. The z-scores for the human CIs and the original target were -9.70 and -9.37 respectively indicating that even in noisy stimuli, observers deploy their fixations efficiently to likely targets rather than casting them randomly hoping to fortuitously find the target.