Assessing two methods of webcam-based eye-tracking for child language research

We assess the feasibility of conducting web-based eye-tracking experiments with children using two methods of webcam-based eye-tracking: automatic gaze estimation with the WebGazer.js algorithm and hand annotation of gaze direction from recorded webcam videos. Experiment 1 directly compares the two methods in a visual-world language task with five to six year-old children. Experiment 2 more precisely investigates WebGazer.js' spatiotemporal resolution with four to twelve year-old children in a visual-fixation task. We find that it is possible to conduct web-based eye-tracking experiments with children in both supervised (Experiment 1) and unsupervised (Experiment 2) settings - however, the webcam eye-tracking methods differ in their sensitivity and accuracy. Webcam video annotation is well-suited to detecting fine-grained looking effects relevant to child language researchers. In contrast, WebGazer.js gaze estimates appear noisier and less temporally precise. We discuss the advantages and disadvantages of each method and provide recommendations for researchers conducting child eye-tracking studies online.