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A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses
IEEE Transactions on Visualization and Computer Graphics
|February 11, 2017
Summary
This study introduces gaze to object mapping (GTOM), or data-of-interest (DOI) analysis, to track what users view in visualizations. This method offers new research possibilities beyond traditional eye-tracking fixation points.
Area of Science:
- Human-Computer Interaction
- Data Visualization
- Cognitive Science
Background:
- Traditional eye-tracking analysis focuses on fixation points or predefined areas of interest (AOI).
- There is a growing need to understand *what* users are looking at, not just *where*.
Purpose of the Study:
- To establish a foundation for data-of-interest (DOI) analysis in eye-tracking research.
- To introduce and define gaze to object mapping (GTOM) as a novel data collection method.
Main Methods:
- Instrumenting visualization code to map gaze coordinates directly to data objects.
- Developing a DOI data model and comparing it to the AOI data model.
- Defining and exemplifying DOI-enabled tasks and experiments across different domains.
Main Results:
- Demonstrated the reliability and low overhead of GTOM/DOI data collection.
- Highlighted the structural and scale differences between DOI and AOI data.
- Presented three concrete examples of DOI experimentation in diverse fields.
Conclusions:
- DOI analysis enables novel research workflows previously not possible with traditional methods.
- Immediate challenges include developing a robust framework for visual support in DOI experimentation and analysis.
