Webcam-based online eye-tracking for behavioral research
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 be...
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| Published in: | Judgment and decision making Vol. 16; no. 6; pp. 1485 - 1505 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Society for Judgment and Decision Making
01.11.2021
Cambridge University Press |
| Subjects: | |
| ISSN: | 1930-2975, 1930-2975 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 1930-2975 1930-2975 |
| DOI: | 10.1017/S1930297500008512 |