The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Visualization of Eye Tracking Data: A Taxonomy and Survey
Abstract
This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.
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Cites
Eye movements in reading and information processing: 20 years of research
Rayner, K. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.3.372 · Cited by 7,979
The eyes have it: a task by data type taxonomy for information visualizations
Shneiderman, B. (2002) · Proceedings 1996 IEEE Symposium on Visual Languages · doi:10.1109/vl.1996.545307 · Cited by 4,535
Yarbus, A. L. (1967) · doi:10.1007/978-1-4899-5379-7 · Cited by 3,304
Eye Tracking: A comprehensive guide to methods and measures
Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R. J., Jarodzka, H. M., & van de Weijer, J. (2015) · Lund University Publications (Lund University) · Cited by 2,919
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
Identifying fixations and saccades in eye-tracking protocols
Salvucci, D. D., & Goldberg, J. H. (2000) · Proceedings of the symposium on Eye tracking research & applications - ETRA '00 · doi:10.1145/355017.355028 · Cited by 2,203
Eye fixations and cognitive processes
Just, M. A., & Carpenter, P. A. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90015-3 · Cited by 1,730
Eye Tracking in Human-Computer Interaction and Usability Research
Jacob, R. J. K., & Karn, K. S. (2003) · The Mind's Eye · doi:10.1016/b978-044451020-4/50031-1 · Cited by 1,567
The plane with parallel coordinates
Inselberg, A. (1985) · The Visual Computer · doi:10.1007/bf01898350 · Cited by 1,423
Eye Tracking Methodology; Theory and Practice
(2007) · Qualitative Market Research: An International Journal · doi:10.1108/13522750710740862 · Cited by 1,313
Cited by
A research agenda for augmented and virtual reality in architecture, engineering and construction
Davila Delgado, J. M., Oyedele, L., Demian, P., & Beach, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101122 · Cited by 486
Exploring Eye Gaze Visualization Techniques for Identifying Distracted Students in Educational VR
Rahman, Y., Asish, S. M., Fisher, N. P., Bruce, E. C., Kulshreshth, A. K., & Borst, C. W. (2020) · 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr46266.2020.00009 · Cited by 45
Ma, J., Yue, H., & Li, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1946196
Metadata
- Authors
- Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., & Ertl, T.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2017-02-09
- Volume
- 36
- Issue
- 8
- Pages
- 260-284
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2586172618
- Cited by
- 285
- References
- 77
- FWCI
- 10.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., & Ertl, T. (2017). Visualization of Eye Tracking Data: A Taxonomy and Survey. Computer Graphics Forum. https://doi.org/10.1111/cgf.13079
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
