The Future Challenges of Eye Tracking Technologies

Special Issue Editors

Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Interests: cognitive engineering; human performance; interface design; situation awareness; ecological interface design; cognitive work analysis

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Guest Editor
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
Interests: data science; eye tracking; psychophysical; security and privacy; virtual and augmented reality

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Co-Guest Editor
Humans and Automation Department, Institute for Energy Technology, 1777 Halden, Norway
Interests: human–AI interaction; eye tracking for training; simulator studies

Special Issue Information

Dear Colleagues,

Eye tracking has become a widely used method for investigating visual attention, cognitive processing, and human performance. Originally developed within experimental psychology and vision science, eye movement recording is now employed across numerous applied domains where understanding how people allocate visual attention has practical relevance. The accessibility of modern eye tracking equipment, including mobile, remote, and headset-based systems, has facilitated its adoption in field settings beyond the laboratory, enabling researchers to study gaze behavior during real-world tasks and in immersive environments. 

This Special Issue aims to garner empirical research, methodological developments, and applied studies that demonstrate how eye movement measures contribute to understanding and improving human performance. Contributions may address questions of usability, training, safety, clinical assessment, or design evaluation where eye tracking serves as the primary input or a complementary research tool.

Research areas may include (but are not limited to) the following:

  • Eye tracking in transportation contexts, including driving, aviation, maritime operations, and rail.
  • Gaze-based evaluations of human–computer interfaces, digital products, and interactive systems.
  • Methods for the gaze-based modeling of human behavior and performance.
  • Clinical and healthcare applications, including diagnostic screening, surgical performance, and patient assessment.
  • Eye tracking in educational settings, training environments, and simulation-based learning.
  • Applications in process control, industrial inspection, and safety-critical operations.
  • Media, advertising, and visual communication research.
  • Sport performance analysis and expertise studies.
  • Consumer behavior and retail research.
  • Assistive technology and gaze-based interaction systems.
  • Methodological considerations for eye tracking i mobile, remote, and VR/AR headset applications.
  • The use of eye tracking within human factors analysis and human reliability assessment.
  • Hardware technologies.
  • Software and data analysis technologies.
  • Ethics and data privacy.
  • Human-related issues including cognitive psychology.
  • AI-related technologies.

We look forward to receiving your contributions.

Dr. Nathan Lau
Dr. Brendan David-John
Michael Hildebrandt
Guest Editors

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Keywords

  • eye tracking
  • visual attention
  • applied research
  • human factors
  • usability
  • human–computer interaction
  • training
  • gaze behavior
  • field studies

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Published Papers (1 paper)

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Research

21 pages, 1034 KB  
Article
Machine Learning Integration of Eye-Tracking and Cognitive Screening for Detecting Cognitive Impairment
by Joan Goset, Clara Mestre, Valldeflors Vinuela-Navarro, Mikel Aldaba, Mar Ariza, Neus Cano, Bàrbara Delàs, Olga Gelonch, Maite Garolera, REHAB Project Collaborative Group and Meritxell Vilaseca
J. Eye Mov. Res. 2026, 19(3), 57; https://doi.org/10.3390/jemr19030057 - 20 May 2026
Viewed by 191
Abstract
Cognitive impairment is common in Post-COVID-19 Condition (PCC), yet full neuropsychological testing remains resource-intensive. Because eye movements are known to be altered in certain cognitive disorders, Eye-Tracking (ET) offers a fast, non-invasive complementary approach for large-scale screening. This study aimed to predict neuropsychological [...] Read more.
Cognitive impairment is common in Post-COVID-19 Condition (PCC), yet full neuropsychological testing remains resource-intensive. Because eye movements are known to be altered in certain cognitive disorders, Eye-Tracking (ET) offers a fast, non-invasive complementary approach for large-scale screening. This study aimed to predict neuropsychological test scores of participants with PCC from ET metrics using machine and deep learning models. ET data was collected from 172 participants performing a battery of visual tasks designed to elicit smooth pursuit and fixational eye movements, as well as pupil responses to light. Cognitive performance was assessed through established neuropsychological tests. We applied regression and classification models (e.g., Random Forest, XGBoost, and deep neural networks) to predict neuropsychological performance. Models were trained using ET data alone and in combination with the Montreal Cognitive Assessment (MoCA) scores, a widely used neuropsychological test for global cognitive screening. Although predicting individual test scores was challenging, combining them into a global composite measure improved performance. Model sensitivity and specificity reached 88% and 34% using ET data alone, and 87% and 60% when integrating ET with MoCA. This last trained model outperformed the conventional MoCA, highlighting the potential of ET as a rapid screening support tool for cognitive assessment. Full article
(This article belongs to the Special Issue The Future Challenges of Eye Tracking Technologies)
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