Cleaning up systematic error in eye-tracking data by using required fixation locations

Abstract

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 (RFLs), in order to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number of implicit 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.

Typical predictive eye movements during action observation without effector-specific motor simulation

“…EyeLink Data Viewer (SR Research, Canada) was used to extract the timing and position of participants' fixations in each trial. To compensate for drift in the eyetracker signal's offset over time, a corrective adjustment was applied to the position of participants' fixations, based on the difference between the raw position of the eye at the beginning of each trial and the position of the fixation cross participants were asked to fixate (Hornof & Halverson, 2002).…”

Calibration algorithm for eyetracking with unrestricted head movement

“…Although small visor shifts cannot be avoided completely, especially during lengthy experimental sessions, their occurrence can be minimized by ensuring that the visor is securely fastened to the participant's head at the beginning of the session, and by using hairpins or some other means to limit slippage. Additionally, Hornof and Halverson (2002) have developed a method that reduces systematic error in eyemovement data by having participants fixate a number of required fixation locations during the course of the experiment and using these data to correct eye-position data offline. Use of this error-correction method together with the eye-calibration procedure developed here could largely eliminate systematic POR estimation errors caused by movement of the eyetracking visor.…”

ASTEF: A simple tool for examining fixations

“…The latter is nothing more than the projection on the screen of the threshold visual angle. Default values are those frequently reported in the literature (Hornof & Halverson, 2002;Jacob & Karn, 2003;Jainta & Jaschinski, 2002;Kramer & McCarley, 2003;Salvucci & Goldberg, 2000): 0.5º to 1º of visual angle and 100-200 msec of duration.…”

“…A noise filter is also implemented, because sporadic points falling outside the fixations may be found during the identification process (Alpern, 1962;Ditchburn, 1980;Hornof & Halverson, 2002). Sometimes, after a first outlier, several other points may fall into the identified fixation.…”