Kandel Margaret, Snedeker Jesse
Department of Psychology, Harvard University, USA.
J Child Lang. 2025 May;52(3):675-708. doi: 10.1017/S0305000924000175. Epub 2024 May 7.
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.
我们使用两种基于网络摄像头的眼动追踪方法，评估对儿童进行基于网络的眼动追踪实验的可行性：使用WebGazer.js算法进行自动注视估计，以及从录制的网络摄像头视频中手动标注注视方向。实验1在一项视觉世界语言任务中，对5至6岁儿童直接比较了这两种方法。实验2在一项视觉注视任务中，对4至12岁儿童更精确地研究了WebGazer.js的时空分辨率。我们发现，在有监督（实验1）和无监督（实验2）的环境下，都可以对儿童进行基于网络的眼动追踪实验——然而，网络摄像头眼动追踪方法在灵敏度和准确性方面存在差异。网络摄像头视频标注非常适合检测与儿童语言研究人员相关的细粒度注视效应。相比之下，WebGazer.js的注视估计显得更嘈杂，时间精度更低。我们讨论了每种方法的优缺点，并为在线进行儿童眼动追踪研究的研究人员提供了建议。
