Workload transition rate matters: Evidence from growth curve modeling

Area of Science:

• Human-Computer Interaction
• Cognitive Psychology
• Aerospace Engineering

Background:

• Workload transition is critical in high-stakes environments like aviation.
• Existing literature has gaps regarding transition rate, theoretical explanations, and performance variability.

Purpose of the Study:

• To investigate the impact of workload transition rate on performance.
• To assess the applicability of current theories to workload transitions.
• To examine performance variability during workload transitions.

Main Methods:

• Sixty Naval flight students performed multitasking in an unmanned aerial vehicle (UAV) control testbed.
• Workload transitions were administered at slow, medium, and fast rates.
• Response time and accuracy were analyzed using growth curve modeling.

Main Results:

• Slow transitions led to the most significant performance decline over time.
• Medium transitions resulted in slower but more accurate and consistent performance.
• Fast transitions yielded faster responses but lower accuracy.
• Significant individual variability in performance trends was observed.

Conclusions:

• Workload transition rate significantly affects performance metrics (speed, accuracy, consistency).
• Performance variability suggests multiple theoretical explanations may be relevant.
• Individual differences likely play a role in performance during workload transitions.
• Provides design guidance for optimizing performance based on transition rate.