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A Closed-Loop Model of Operator Visual Attention, Situation Awareness, and Performance Across Automation Mode

Aaron W Johnson1, Kevin R Duda2, Thomas B Sheridan

  • 1Massachusetts Institute of Technology, Cambridge.

Human Factors
|September 4, 2016
PubMed
Summary

This study introduces a human-vehicle model to understand how visual attention shifts during lunar landing control transitions. The model confirms that uncertainty in system state estimates influences operator attention and situation awareness.

Keywords:
attentional processeshuman performance modelinghuman–automation interactionmathematical modelingsituation awarenesssupervisory control

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Area of Science:

  • Human-Factors Engineering
  • Cognitive Psychology
  • Aerospace Systems

Background:

  • Control mode transitions, such as from autopilot to manual flight, can overload operators.
  • Increased attentional demands lead to reallocation and reprioritization of resources.
  • This can heighten uncertainty in operator estimates, reducing situation awareness.

Purpose of the Study:

  • To develop and validate an integrated human-vehicle model.
  • To investigate cognitive processes influencing visual attention, workload, and situation awareness.
  • To analyze changes during control mode transitions in a simulated lunar landing.

Main Methods:

  • Integrated human-vehicle model combining optimal control, crossover, and SEEV models.
  • Modified SEEV (salience, effort, expectancy, value) model to incorporate state uncertainty.
  • Validation using simulated human-in-the-loop lunar landing data.

Main Results:

  • Model demonstrated high accuracy in predicting attention allocation (≤3.6% difference).
  • Model successfully mimicked experimental data for mental workload and situation awareness.
  • Validated the hypothesis that visual attention is driven by system state uncertainty.

Conclusions:

  • Visual attention is significantly influenced by the uncertainty in an operator's system state estimates.
  • System state uncertainty is a valuable metric for designers to predict attention and situation awareness shifts.
  • The model provides insights into operator cognitive processes during critical control transitions.