Eye-tracking metrics for estimating workload and characterizing errors in conflict detection and resolution during...

Area of Science:

• Human Factors
• Cognitive Engineering
• Aviation Psychology

Background:

• Increasing air traffic necessitates advanced tools to manage controller overload and prevent mishandled conflicts.
• Ocular behavior provides a continuous, non-intrusive measure of both global mental workload and attentional focus.

Purpose of the Study:

• To determine if eye-tracking metrics can estimate mental workload (MWL) in air traffic controllers.
• To identify how eye movements relate to errors in conflict detection and resolution during simulated air traffic control.

Main Methods:

• Twenty-four novice participants performed simulated air traffic control tasks with varying traffic loads and complexity.
• Eye-tracking recorded pupil diameter, blink dynamics, and fixation patterns; subjective MWL was assessed using standard scales.

Main Results:

• Higher traffic density and complexity increased self-reported MWL, pupil size, and blink rate reduction.
• Blink rate and pupil size were strong predictors of MWL, explaining up to 94% of the variance.
• Ocular metrics differentiated successful conflict resolution from errors, linked to fixation duration and intervention frequency.

Conclusions:

• Ocular indices offer precise estimation of controller MWL.
• Gaze behavior and action patterns can predict errors in conflict detection and resolution.
• Integrating these findings into air traffic control systems can enable real-time workload management and proactive safety interventions.