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Related Experiment Video

Updated: Sep 2, 2025

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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Modeling eye movement in dynamic interactive tasks for maximizing situation awareness based on Markov decision

Shuo Ma1, Jianbin Guo1, Shengkui Zeng1

  • 1School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China.

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|August 2, 2022
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Summary

This study introduces a new eye movement model for complex tasks, aiming to maximize situation awareness (SA). The model accurately predicts operator eye movements by considering information expectancy and value, validated in a flight simulation.

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Aerospace Engineering

Background:

  • Operators in complex dynamic tasks require high situation awareness (SA) for optimal performance.
  • Existing eye movement models often neglect SA and are limited to static or simple dynamic tasks.
  • SA failures can lead to degraded task performance and system accidents.

Purpose of the Study:

  • To propose a novel eye movement model for dynamic interactive tasks that prioritizes maximizing situation awareness (SA).
  • To model the dynamic eye movements of experienced operators in complex interactive tasks.
  • To incorporate top-down factors like expectancy and value into eye movement prediction.

Main Methods:

  • Developed an eye movement model using Markov decision process (MDP).
  • Integrated expectancy (information update probability) and value (information importance) as key factors.
  • Treated sequences of eye fixations as decisions to maximize SA-related rewards under uncertainty.
  • Validated the model using a flight simulation experiment.

Main Results:

  • The proposed MDP-based eye movement model effectively captures operator behavior in dynamic interactive tasks.
  • Model predictions showed high correlation with experimental data for fixation probabilities ([Formula: see text]) and transitions ([Formula: see text]).
  • The inclusion of expectancy and value significantly improved the model's ability to predict SA-driven eye movements.

Conclusions:

  • The novel eye movement model successfully maximizes situation awareness (SA) in complex dynamic interactive tasks.
  • The model provides a robust framework for understanding and predicting operator eye movements by incorporating cognitive factors.
  • This research has implications for designing better human-machine interfaces and training programs for operators in critical domains.