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Webcam-based online eye-tracking for behavioral research

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  • Yang, Xiaozhi
  • Krajbich, Ian

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 them used 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 JavaScript library widely used by 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.

Suggested Citation

  • Yang, Xiaozhi & Krajbich, Ian, 2021. "Webcam-based online eye-tracking for behavioral research," Judgment and Decision Making, Cambridge University Press, vol. 16(6), pages 1485-1505, November.
    • Handle: RePEc:cup:judgdm:v:16:y:2021:i:6:p:1485-1505_7
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