Applied Eye-Tracking: From Cognition to Research Practice 2500-EN-CS-EM-06

This hands-on course teaches students what eye-tracking is, how and when to use it. It covers the entire research process, from understanding how human vision works to designing experiments, analyzing data, and presenting results.

The course is divided into four main parts over 12 weeks (24 hours total):

- Foundations of Vision & Eye-Tracking (3 weeks): Students learn how the eye and brain process visual information. We cover the anatomy of vision, attentional systems, and the history and modern applications of eye-tracking.
- Metrics, Data & Software (2 weeks): Students learn how gaze behavior translates into data. We cover key metrics (such as fixation duration), Areas of Interest (AOIs), and visual outputs (scanpaths, heatmaps). We also introduce experimental software.
- Hands-On Lab Practice (2 weeks): Students get direct experience with real hardware. They learn to set up, calibrate, and record data using Tobii Pro Glasses 3 and Pupil Core eyetrackers.
- Team Research Project (5 weeks): Students work in small teams to run an original study. Teams design their experiment, collect and clean data in class, analyze their results, and present their findings to the class.

Learning outcomes

Upon successful completion of this course, the student will be able to:

1. Explain how different paradigms in cognitive science apply to eye-tracking research, and how interacting cognitive systems shape gaze behavior (K_W01, K_W05)
2. Describe the structural, functional, and neurophysiological mechanisms of the human visual and attentional systems that influence gaze behavior (K_W02)
3. Critically evaluate the strengths, limitations, and methodological assumptions of using eye-tracking as a quantitative or qualitative method to study cognitive processes and human behavior in various applied contexts (K_W03, K_U03, K_K01)
4. Design and conduct an eye-tracking experiment using different experimental setups (stationary and/or wearable systems) to investigate a specific cognitive or practical hypothesis, while adhering to ethical research standards (K_W07, K_U04, K_U15, K_K06)
5. Process multidimensional eye-tracking datasets by applying filtering, data-cleaning, and data-export techniques (including Areas of Interest (AOIs)) and interpret data visualization (heatmaps, gaze plots) (K_W07, K_U06, K_U07, K_U12)
6. Formulate and clearly present evidence-based research findings and actionable practical recommendations within an interdisciplinary team environment, delivering a final presentation in English (K_U11, K_U14)

Assessment criteria

Assessment methods:
Final Group Project

Components of the final grade and their weights:
Final Group Project (Presentation & Report) 80%
Class Participation: 20%
Note: Once the base grade is established, attendance is checked. For each absence exceeding the allowed limit of 2, the final grade is lowered by 0.5.

Grading scale:
over 50%: 3
over 60%: 3+
over 70%: 4,
over 80%: 4+
over 90%: 5

Requirements for retaking the assessment:
Students who fail the final group project (and have no more than 2 absences) must complete the retake by:
Submitting an individual eye-tracking research project.
Taking a written test covering the theoretical course material.

Exams in the exam session:
n/a

Attendance at classes is mandatory. Throughout the semester, 2 absences are allowed. For each additional absence, the final grade will be lowered by half a grade.

Bibliography

01 Course Introduction & Foundations of Eye-Tracking: introduction to the course format, final project rules, and basic concepts in eye-tracking.

02 Anatomy and Neurophysiology of Vision & Cognition: how the visual system works: foveal vs. peripheral vision, visual acuity, and attentional mechanisms (top-down vs. bottom-up) driving gaze behavior.

03 ET History & Industry Applications: evolution of eye-tracking technology. Overview of modern setups (stationary vs. wearable) and applied use cases in UX, HCI, marketing, and cognitive research.

04 Understanding Eye-Tracking Data: Processing, Metrics, AOIs & Visualizations

05 Methodology & Study Design Do's and Don'ts: formulating research questions, operationalization, and selecting appropriate paradigms.

06 Hands-on Laboratory Classes: Hardware Setup & Calibration, Data Recording & Export

07 Translating research ideas into viable eye-tracking hypotheses and preparing team eye-tracking projects.

08 Final Presentations

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During the course, selected passages from the readings listed below will be discussed. Detailed information on the fragments will be provided during the class:

Holmqvist, K., & Andersson, R. (2017). Eye tracking: A comprehensive guide to methods. Paradigms and measures, 3(5).
Duchowski, A. T., & Duchowski, A. T. (2017). Eye tracking methodology: Theory and practice. Springer.
Hessels, R.S., Nuthmann, A., Nyström, M. et al. The fundamentals of eye tracking part 1: The link between theory and research question. Behav Res 57, 16 (2025). https://doi.org/10.3758/s13428-024-02544-8
Hooge, I.T.C., Nuthmann, A., Nyström, M. et al. The fundamentals of eye tracking part 2: From research question to operationalization. Behav Res 57, 73 (2025). https://doi.org/10.3758/s13428-024-02590-2
Niehorster, D.C., Nyström, M., Hessels, R.S. et al. The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study. Behav Res 57, 46 (2025). https://doi.org/10.3758/s13428-024-02529-7
Hooge, I.T.C., Nyström, M., Niehorster, D.C. et al. The fundamentals of eye tracking part 6: Working with areas of interest. Behav Res 58, 65 (2026). https://doi.org/10.3758/s13428-025-02937-3
Beesley, Tom & Pearson, Daniel & Pelley, Mike. (2019). Eye Tracking as a Tool for Examining Cognitive Processes. 10.1016/B978-0-12-813092-6.00002-2.
Yarbus, Alfred L. (1967). Eye movements and vision. New York: Plenum Press. ISBN 978-1-4899-5379-7.
Hoffman, James E. (2016). "Visual attention and eye movements". In Pashler, H. (ed.). Attention. Studies in Cognition. Taylor & Francis. pp. 119–153.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: from eye fixations to comprehension. Psychological review, 87(4), 329–354.
Anderson, J. R., Bothell, D., & Douglass, S. (2004). Eye movements do not reflect retrieval processes: Limits of the eye-mind hypothesis. Psychological Science, 15(4), 225-231.
Kröger, J. L., Lutz, O. H.-M., & Müller, F. (2020). What does your gaze reveal about you? On the privacy implications of eye tracking. In M. Friedewald, M. Önen, E. Lievens, S. Krenn, & S. Fricker (Eds.), Privacy and identity management. Data for better living: AI and privacy (pp. 226–241). Springer. https://doi.org/10.1007/978-3-030-42504-3_15
Płużyczka, M. (2018). The first hundred years: A history of eye tracking as a research method. Applied Linguistics Papers, 25(4), 101–116.
Aslin, R. N. (2007). What's in a look?. Developmental science, 10(1), 48-53.
Godfroid, A., & Hui, B. (2020). Five common pitfalls in eye-tracking research. Second Language Research, 36(3), 277–305.
Godwin, H. J., Hout, M. C., Alexdóttir, K. J., Walenchok, S. C., & Barnhart, A. S. (2021). Avoiding potential pitfalls in visual search and eye-movement experiments: A tutorial review. Attention, Perception, & Psychophysics, 83(7), 2753–2783.
Eckstein, M. K., Guerra-Carrillo, B., Singley, A. T. M., & Bunge, S. A. (2017). Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?. Developmental cognitive neuroscience, 25, 69-91.
Loftus, G. R., & Mackworth, N. H. (1978). Cognitive determinants of fixation location during picture viewing. Journal of Experimental Psychology: Human Perception and Performance, 4(4), 565–572.
Theeuwes, J., Belopolsky, A., & Olivers, C. N. L. (2009). Interactions between working memory, attention and eye movements. Acta Psychologica, 132(2), 106–114.

Notes

Term 2026Z:

This is an entry-level course. No prior experience with eye-tracking, neuroscience, or coding is required. All theoretical concepts and practical tools will be introduced from scratch during the classes. Familiarity with basic statistics (e.g., mean, median, percentages) is expected.

Students must respect the principles of academic integrity. Cheating and plagiarism (including copying work from other students, internet or other sources) are serious violations that are punishable and instructors are required to report all cases to the administration.

Generative AI tools (e.g., ChatGPT, Claude) are permitted for brainstorming research ideas and study designs, improving English language quality and proofreading final presentations/reports, writing and/or troubleshooting data analysis scripts.

Any use of AI must be explicitly disclosed in the methodology section or project bibliography (stating which tool was used and for what purpose). Submitting AI-generated text or data analysis as original personal work without proper declaration is prohibited.