A simple nonparametric method for classifying eye fixations

There is no standard method for classifying human 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.The Matlab® routine NonParaFixLab.m was created to provide a simple, parameter-free method of classifying eye fixations by exploiting some of their more general distributional properties. The method is primarily speed-based, but, by contrast with many other methods, the optimum speed threshold for classifying saccades— and therefore fixations—was derived automatically from the data for each observer and stimulus individually. The derivation was founded on Tibshirani, Walther, and Hastie’s ‘gap statistic’ for identifying the optimum number of clusters in a set of data. Because speed-based methods can generate what appear to be unphysiologically short fixations, usually attributed to instrumental noise, the proposed nonparametric method was extended to include an optimum duration threshold—also derived automatically from the data—as part of the classification.To run NonParaFixLab.m, please consult the separate README.TXT file.