Reporting Eye-Tracking Data Quality: Towards a New Standard
Author:
Affiliation:
1. University of Zurich, Switzerland
2. University of Potsdam, Germany
3. University of Zurich, Switzerland and University of Potsdam, Germany
Funder
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Bundesministerium für Bildung und Forschung
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3649902.3655658
Reference22 articles.
1. Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more
2. Sequence Classification with Human Attention
3. Jon W. Carr. 2023. eyekit: A lightweight Python package for doing open transparent reproducible science on reading behavior. https://github.com/jwcarr/eyekit
4. Algorithms for the automated correction of vertical drift in eye-tracking data
5. Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding
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