Abstract
Webcam eye tracking has already become a powerful tool for online behavioral research, and the first generation of Labvanced's system was validated against an EyeLink 1000 in the laboratory at an accuracy of about 1.4° of visual angle (Kaduk et al., 2023). Several questions remained open. How much of that accuracy survives the uncontrolled conditions of a real online study? Why do some participants deliver far worse data than others, and can they be identified before a researcher pays for them? Can the calibration be shortened to make it more practical in ordinary online studies? And what should happen to participants who do not pass it, or otherwise do not qualify? Here we show how we improved eye-tracking accuracy, while simultaneously shortening the calibration, simplifying the setup, and optimizing the whole procedure for real online data collection with our partner Prolific. We then validated the system in seven recording batches with about 500 completed participants of a 45-target accuracy task, and publish their anonymized data so that every result can be verified. With our new standard calibration (about 2 minutes) and balanced screening, the median participant's error was 5.2 % of the screen diagonal, 1.8° of visual angle on a laptop at 60 cm. And with the 5 minute calibration and very strict screening, the median accuracy of online participants reached 4 % of the screen diagonal, which is equal to 1.4° of visual angle at a standard distance. In summary, with this release of Eye Tracking 2.0, we show that the accuracy reported in our peer-reviewed laboratory comparison can be replicated online