A simple nonparametric method for classifying eye fixations

Abstract

There is no standard method for classifying eye fixations. Thresholds for speed, acceleration, duration, and stability of point of gaze have each been employed to demarcate data, but they have no commonly accepted values. Here, some general distributional properties of eye movements were used to construct a simple method for classifying fixations, without parametric assumptions or expert judgment. The method was primarily speed-based, but the required optimum speed threshold was derived automatically from individual data for each observer and stimulus with the aid of Tibshirani, Walther, and Hastie's 'gap statistic'. An optimum duration threshold, also derived automatically from individual data, was used to eliminate the effects of instrumental noise. The method was tested on data recorded from a video eye-tracker sampling at 250 frames a second while experimental observers viewed static natural scenes in over 30,000 one-second trials. The resulting classifications were compared with those by three independent expert visual classifiers, with 88-94% agreement, and also against two existing parametric methods. Robustness to instrumental noise and sampling rate were verified in separate simulations. The method was applied to the recorded data to illustrate the variation of mean fixation duration and saccade amplitude across observers and scenes. © 2011 Elsevier Ltd