Eye-Based Cognitive Overload Prediction in Human-Machine Interaction via Machine Learning

Maria Trigka, Elias Dritsas, Phivos Mylonas

2025

Abstract

Cognitive overload significantly affects human performance in complex interaction settings, making its early detection essential for designing adaptive systems. This study investigated whether gaze-derived features can reliably predict overload states using supervised machine learning (ML). The analysis was conducted on an eye-tracking dataset from a cognitively demanding visual task that incorporated fixations, saccades, and pupil diameter measurements. Five classifiers, namely, Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), and Multilayer Perceptron (MLP), were evaluated using stratified train/test splits and 5-fold cross-validation. XGB achieved the best performance, with an accuracy of 0.902, a precision of 0.958, a recall of 0.821, an F1 score of 0.884, and an area under the ROC curve (AUC) of 0.956. These findings confirm that gaze-derived features alone can reliably distinguish between cognitive overload states. The results also revealed trade-offs between simple models, which are easier to interpret but more conservative, and complex models, such as XGB and MLP, which achieved stronger predictive performance. Future studies should address subject-independent validation, incorporate temporal modeling of gaze dynamics, and explore personalization and cross-task generalization to advance robust and adaptive cognitive monitoring.

Paper Citation

in Harvard Style

Trigka M., Dritsas E. and Mylonas P. (2025). Eye-Based Cognitive Overload Prediction in Human-Machine Interaction via Machine Learning. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 565-572. DOI: 10.5220/0013782800003985

in Bibtex Style

@conference{webist25,
author={Maria Trigka and Elias Dritsas and Phivos Mylonas},
title={Eye-Based Cognitive Overload Prediction in Human-Machine Interaction via Machine Learning},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={565-572},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013782800003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Eye-Based Cognitive Overload Prediction in Human-Machine Interaction via Machine Learning
SN - 978-989-758-772-6
AU - Trigka M.
AU - Dritsas E.
AU - Mylonas P.
PY - 2025
SP - 565
EP - 572
DO - 10.5220/0013782800003985
PB - SciTePress