Eye-Tracking Based Estimation of Cognitive Load across Languages: A Pilot Study

We explored cognitive load and reading efficiency across different languages using eye-tracking metrics (fixation duration and visit count). A random forest regressor obtain strong predictive performance (MAE = 5.72, R² = 0.91). Results highlight language-specific differences and emphases eye-tracking combined with machine learning as effective tools for studying cognitive processing.