Document Type

Article

Publication Date

5-1-2021

City

Corvallis

State

OR

Abstract

Characterizing and predicting pilot cognitive workload remains a formidable challenge, especially in tasks with a high perceptual/motor demand like aerial refueling. Cognitive models are useful tools for this, as they offer the potential to derive both performance and workload simulations before a test is conducted. We conducted a task analysis of a C-17 aerial refueling mission and developed a low-fidelity Atomic Components of Thought – Rational (ACT-R) model and environment to simulate the task. ACT-R models have been successful in predicting workload in other domains, such as menu navigation and problem solving. Eight aerial maneuvers were examined, including takeoff, climb, cruise, descent, refueling, contact station keeping, and landing. The exercise revealed two subtasks not currently described in great detail by workload modeling methods: trajectory estimation and collision avoidance. We identify gaps in research on workload modeling approaches and explore preliminary predictions made by the model using default ACT-R parameters.


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