Explainable Eye-Tracking-Based Cognitive Workload
Classification for Interactive Visual Tasks: A Reproducible
Human-Computer Interaction Study Using the Public COLET
Dataset
Mahmoud A. Zaher1, Nabil M. Eldakhly2
1Asso. prof. Faculty of Artificial Intelligence and Information, Horus University (HUE), Egypt
2Asso. prof. Faculty of Computers and Information, Egypt
Emails: mzaher@horus.edu.eg; nabil.omr@sadatacademy.edu.eg
Abstract
Attention allocation, efficiency of interactions and the formation of errors during human-computer interaction
(HCI) are directly influenced by cognitive workload. Eye tracking provides a feasible, non-invasive source
of evidence to estimate workload since the behavior of gaze is strongly correlated with visual search, task
processing and decision effort. The paper explores explainable cognitive workload classification based on explainable
cognitive workload on the public COLET dataset; eye-tracking recordings of 47 subjects completing
interactive search tasks of the visual-search with workload labels based on NASA-TLX. The five supervised
learning models are tested on binary and four-class problems, and the most successful setup is analyzed via
SHAP-based feature attribution. In both tasks, boosting-based ensembles are best at predictive behavior, with
XGBoost scoring highest on the overall and binary low-v-high discrimination scores in the best range of performance
reported in the original COLET benchmark. The feature analysis attribute shows that the most
significant variables are gaze entropy, fixation time, pupil changes, and saccadic movements. The results are
consistent with the application of explainable gaze-based models to adaptive interfaces that can adapt to a
rising mental load by making the content simpler to present, varying the pacing, or attentive to important
information.
Keywords: Cognitive workload; Eye tracking; Human-computer interaction; Explainable artificial intelligence;
XGBoost; Adaptive interfaces