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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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Society for Judgment and Decision Making
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4df8599f5c77405c858dca35b8ca4f97 |
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| Sumario: | 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 and 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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