Noise in the Machine: Sources of Physical and Computation Error in Eye Tracking with Pupil Core Wearable Eye Tracker

Developments in wearable eye tracking devices make them an attractive solution for studies of eye movements during naturalistic head/body motion. However, before these systems’ potential can be fully realized, a thorough assessment of potential sources of error is needed. In this study, we examine three possible sources for the Pupil Core eye tracking goggles: camera motion during head/body motion, choice of calibration marker configuration, and eye movement estimation. In our data, we find that up to 36% of reported eye motion may be attributable to camera movement; choice of appropriate calibration routine is essential for minimizing error; and the use of a secondary calibration for eye position remapping can improve eye position errors estimated from the eye tracker.