Date of Award

Spring 2024

Document Type


Degree Name

Master of Science (MS)



Committee Director

Yusuke Yamani

Committee Member

Xiao Yang

Committee Member

Michelle L. Kelley


Advances in automation and aviation technologies have been catalysts for the emerging market of Advanced Air Mobility (AAM), an ecosystem of novel aircraft concepts including package delivery drones and passenger carrying air-taxis. Future aircraft operators in this environment will be tasked with remotely supervising multiple highly automated aircraft on a visual interface while receiving less training than traditional pilots. More research should explore how an operator’s potentially limited understanding of an automated system affects visual performance and interactions between human operators and AAM technologies. This study examined the influence of mental models of an autopilot system on visual attention allocation for participants managing multiple vehicles in a low-fidelity AAM simulation environment. Fifty-five participants completed a series of multi-aircraft control scenarios after reading training slides with or without explicit information on the underlying functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively) with their eye movements recorded. The results indicated that the Advanced Mental Model group allocated significantly more visual attention to a supplemental data display than the Basic Mental Model group. Surprisingly, participants allocated more visual attention to the supplemental data display with low than high information bandwidth, which was opposite of the predicted effect. The results also indicated a significant interaction between expectancy and value parameters in the SEEV model, providing additional evidence in support of this theoretical debate. In practice, results from this study show that refining mental models through a simple training program could be an effective approach to alter AAM operators’ visual scan behaviors.


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