Webcam-based online eye-tracking for behavioral research
Xiaozhi Yang, Ian Krajbich
Abstract
Experiments are increasingly moving online. This poses a major challenge for researchers who rely on in-lab techniques such as eye-tracking. Researchers in computer science have developed web-based eye-tracking applications (WebGazer; Papoutsaki et al., 2016) but they have yet to see use in behavioral research. This is likely due to the extensive calibration and validation procedure, inconsistent temporal resolution (Semmelmann & Weigelt, 2018), and the challenge of integrating it into experimental software. Here, we incorporate WebGazer into a widely used JavaScript library among behavioral researchers (jsPsych) and adjust the procedure and code to reduce calibration/validation and improve the temporal resolution (from 100-1000 ms to 20-30 ms). We test this procedure with a decision-making study on Amazon MTurk, replicating previous in-lab findings on the relationship between gaze and choice, with little degradation in spatial or temporal resolution. This provides evidence that online web-based eye-tracking is feasible in behavioral research.