Running Head: Cleaning up eye tracking data using RFLs Cleaning up systematic error in eye tracking data by using required fixation locations

In the course of running an eye tracking experiment, one computer system or subsystem typically presents the stimuli to the participant and records manual responses, and another collects the eye movement data, with little interaction between the two during the course of the experiment. This article demonstrates how the two systems can interact with each other to facilitate a richer set of experimental designs and applications, and to produce more accurate eye tracking data. In an eye tracking study, a participant is periodically instructed to look at specific screen locations, or explicit required fixation locations, such as to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number of implicit required fixation locations (RFLs)--screen locations that the participant must look at within a certain window of time or at a certain moment in order to successfully and correctly accomplish a task, but without explicit instructions to fixate those locations. In these windows of time or at these moments, the disparity between the fixations recorded by the eye tracker and the screen locations corresponding to implicit RFLs can be examined, and the results of the comparison can be used for a variety of purposes. This article shows how the disparity can be used to monitor the deterioration in the accuracy of the eye tracker calibration, and to automatically invoke a recalibration procedure when necessary. This article also demonstrates how the disparity will vary across screen regions and participants, and how each participant’s unique error signature can be used to reduce the systematic error in the eye movement data collected for that participant.