Related Experiment Video
Updated: Feb 8, 2026
07:09
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
Published on: November 14, 2018
11.5K
Comprehensive dataset of features describing eye-gaze dynamics across multiple tasks.
Rujeena Mathema1,2, Shamimeh M Nav1,2, Shailendra Bhandari3,4
1Department of Computer Science, OsloMet - Oslo Metropolitan University, P.O. Box 4 St. Olavs plass, 0130, Oslo, Norway.
Scientific Data
|February 6, 2026
Summary
This study introduces a new eye-gaze dynamics dataset from 251 participants, featuring saccades, blinks, and pupil size. This resource aids research in visual attention and cognitive science.
Area of Science:
- Cognitive Science
- Neuroscience
- Human-Computer Interaction
Background:
- Eye-gaze dynamics are crucial for understanding visual attention and cognitive processes.
- Existing datasets may lack the diversity of tasks or features required for comprehensive analysis.
- High-resolution, timestamped eye-tracking data is essential for detailed behavioral studies.
Purpose of the Study:
- To present a comprehensive dataset of human eye-gaze dynamics.
- To facilitate research in areas such as oculomotor control, perceptual processing, visual attention, and cognitive science.
- To provide a structured, anonymized dataset suitable for various analytical applications.
Main Methods:
- Collected data from 251 participants performing tasks like vanishing saccade, cued saccade, flickering cross, rotating ball, and free viewing.
- Utilized EyeLink Portable Duo eye-tracker hardware, recording at 1000 Hz.
- Processed raw EyeLink Data Format (EDF) files into structured, anonymized data following SIKT ethical standards.
Main Results:
- A comprehensive dataset of eye-gaze dynamics, including timestamped gaze coordinates, pupil sizes, and event classifications (fixations, saccades, blinks).
- Data covers diverse experimental paradigms relevant to eye-tracking research.
- An automated pipeline ensured efficient processing and structuring of the recorded data.
Conclusions:
- The presented dataset offers a valuable resource for studying visual attention, cognitive processing, and oculomotor control.
- Its diverse features and tasks support a wide range of research applications, including assistive technology development.
- The dataset's comprehensive nature and adherence to ethical standards make it suitable for advancing human-computer interaction and cognitive science research.
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