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Discriminative Capabilities of Eye Gaze Measures for Cognitive Load Evaluation in a Driving Simulation Task
Anastasiia Bakhchina1, Karina Arutyunova1, Evgenii Burashnikov1
1Cognitive Systems Lab, Harman Research, Harman International, 30001 Cabot Dr, Novi, MI 48377, USA.
Journal of Eye Movement Research
|January 21, 2026
Summary
Eye tracking metrics effectively measure cognitive load during driving, complementing subjective reports. This objective method enhances driver state prediction across various road conditions.
Area of Science:
- Cognitive psychology
- Human-computer interaction
- Transportation safety
Background:
- Driving requires significant cognitive resources, increasing cognitive load.
- Objective measures of cognitive load are needed to supplement subjective self-report scales.
Purpose of the Study:
- To compare the effectiveness of eye tracking metrics versus the NASA-Task Load Index (NASA-TLX) in distinguishing cognitive load levels during simulated driving.
- To explore the utility of eye gaze metrics for assessing cognitive load dynamics and driver state prediction.
Main Methods:
- 685 participants completed highway and urban driving tasks in a simulator.
- The N-Back test was employed to manipulate cognitive load.
- Eye gaze data and NASA-TLX scores were collected and analyzed.
Main Results:
- NASA-TLX accurately distinguished high and low cognitive load (0.81 accuracy on highways).
- Eye gaze metrics excelled at differentiating highway vs. urban driving (0.82 accuracy).
- Gaze transition entropy showed high accuracy (0.95) for cognitive load dynamics in urban driving.
Conclusions:
- Eye gaze metrics offer objective, dynamic assessment of cognitive load during driving.
- Combining eye tracking with self-reports can improve driver state prediction algorithms.
- Objective eye gaze measures show promise for enhancing road safety systems.
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