Mode-of-disparities error correction of eye-tracking data

Abstract

In eye-tracking research, there is almost always a disparity between a person's actual gaze location and the location recorded by the eye tracker. Disparities that are constant over time are systematic error. In this article, we propose an error correction method that can reliably reduce systematic error and restore fixations to their true locations. We show that the method is reliable when the visual objects in the experiment are arranged in an irregular manner-for example, when they are not on a grid in which all fixations can be shifted to adjacent locations using the same directional adjustment. The method first calculates the disparities between fixations and their nearest objects. It then uses the annealed mean shift algorithm to find the mode of the disparities. The mode is demonstrated to correctly capture the magnitude and direction of the systematic error so that it can be removed. This article presents the method, an extended demonstration, and a validation of the method's efficacy.