Maria Pombo¹, Jan W. Kurzawski¹, Augustin Burchell¹, Nina M. Hanning², Simon Liao¹, Najib J. Majaj¹, Denis G. Pelli¹; ¹New York University, ²Humboldt Universität zu Berlin

Compared to in-lab testing, online methods allow easier and faster testing of large, more diverse populations. Many psychophysical measurements, including visual crowding, require accurate eye fixation. Accurate fixation is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. However, both approaches are impractical online as online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision (±4 deg). EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking (Kurzawski & Pombo et al., Frontiers in Human Neuroscience 2023). Observers are tasked with using their cursor to track a moving crosshair, and at a random time during successful tracking, a brief peripheral target is presented. Then the observer responds by identifying the target. To evaluate EasyEyes fixation accuracy and thresholds, we tested 12 naive observers in three ways in a counterbalanced order: first, in the lab, using gaze-contingent stimulus presentation; second, in the lab, using EasyEyes while independently monitoring gaze using EyeLink 1000; third, online at home, using EasyEyes. We find that crowding thresholds are consistent and individual differences are conserved. The small root mean square (RMS) fixation error (0.6 deg) during target presentation gets around the need for gaze tracking. Thus, this method enables fixation-dependent measurements online, for easy testing of larger and more diverse populations. Within our sample (N = 12), one observer (S9) peeked. S9 had the highest RMSE between the crosshair and cursor and the most frames with unsuccessful tracking, suggesting that peeking and tracking behavior are associated. We are now assessing whether the accuracy of cursor tracking is a good predictor of “peeking” as a way of detecting peeking.

Acknowledgements: Funding: NIH grants R01-EY027964 (DP), R01-EY031446 (NM) and P30-EY013079 (core), and a Marie Skłodowska-Curie individual fellowship by the European Commission (898520) (NH). Development of EasyEyes software was developed in part with funds provided by Meta through a sponsored research agreement.