Experiments are increasingly moving online. This poses a major challenge forresearchers who rely on in-lab techniques such as eye-tracking. Researchers incomputer science have developed web-based eye-tracking applications (WebGazer;Papoutsaki et al., 2016) but they have yet to see them used in behavioralresearch. This is likely due to the extensive calibration and validationprocedure, inconsistent temporal resolution (Semmelmann & Weigelt, 2018),and the challenge of integrating it into experimental software. Here, weincorporate WebGazer into a JavaScript library widely used by behavioralresearchers (jsPsych) and adjust the procedure and code to reducecalibration/validation and improve the temporal resolution (from 100–1000ms to 20–30 ms). We test this procedure with a decision-making study onAmazon MTurk, replicating previous in-lab findings on the relationship betweengaze and choice, with little degradation in spatial or temporal resolution. Thisprovides evidence that online web-based eye-tracking is feasible in behavioralresearch.
