Not all fixations are created equal: The benefits of using ex-Gaussian modeling of fixation durations.

Abstract

Various cognitive and perceptual factors have been shown to modulate the duration of fixations during visual exploration of complex scenes. The majority of these studies have only considered the mean of the distribution of fixation durations. However, this distribution is skewed to the right, so that an increase in the mean may be driven by a lengthening of all fixations (i.e., a right shift of the whole distribution) or only the relatively longer ones (i.e., a longer right tail of the distribution). To determine which factor is at play, the distribution can be modeled with an ex-Gaussian distribution, which is a convolution of a Gaussian and an exponential distribution. Here we demonstrate the usefulness of applying the ex-Gaussian model to empirical distributions of fixation durations and the reliability of its parameters across time. We demonstrate how the ex-Gaussian model had advantages over exclusive consideration of the mean, by showing that an increase in the mean can stem from specific changes in the components of the ex-Gaussian distribution. Specifically, the type of image leads to a change in the Gaussian component alone, indicating a right shift of the main mass of the distribution. By contrast, familiarity with the inspected image modifies the exponential component, and results in a more specific modulation of a subset of relatively long fixations. Hence, estimating the ex-Gaussian parameters may provide novel insights into the underlying processes that determine fixation duration and can contribute to the future development of process-based computational models of gaze behavior.