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A Design Space of Vision Science Methods for Visualization Research.
IEEE Transactions on Visualization and Computer Graphics
|October 22, 2020
Summary
This study introduces a framework of experimental methods to understand how people perceive data visualizations. It aims to improve visualization design by integrating vision science techniques.
Area of Science:
- Human-Computer Interaction
- Cognitive Science
- Data Visualization
Background:
- Existing visualization research uses limited methods to study visual perception.
- Vision science offers diverse experimental tools for analyzing how users process visualizations.
- A curated collection of these methods is lacking in current research.
Purpose of the Study:
- To introduce a design space of experimental methods for empirical investigation of visualization perception.
- To inform evidence-based visualization design guidelines.
- To provide a shared lexicon for experimental visualization research.
Main Methods:
- Discusses experimental paradigms, adjustment types, response types, and dependent measures from vision science.
- Applies these methods to data visualization examples.
- Analyzes the advantages and limitations of each technique.
Main Results:
- Presents a structured design space for experimental visualization research.
- Facilitates the creation of innovative studies.
- Highlights the potential for deeper collaboration between visualization and vision science.
Conclusions:
- Advocates for a stronger integration of vision science methodologies into visualization research.
- Enables more rigorous scientific understanding of visualization design and evaluation.
- Extends the methodological toolkit for studying visualization and visual perception.
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