A Bayesian Approach to Understanding the Relationship Between Self-Reported Mental Workload and Asset Similarity in Collaborative Combat Aircraft Environments

Abstract

Autonomous Collaborative Platforms, such as Collaborative Combat Aircraft (CCA) will increase in use in United States Air Force (USAF) operations. Research focused on CCA management will be met with participant recruitment challenges. Military contexts are highly complex environments where the availability of well-trained operators for research participation is limited due to competing operational mission priorities and small populations. To overcome this challenge, we highlight the benefits of a Bayesian statistical approach, which can leverage prior experimental data to mitigate statistical power concerns. We applied this approach to examine the relationship between self-reported mental workload and asset similarity in CCA missions, drawing on prior experimental data from a related study. Using a high-fidelity manned-unmanned teaming simulator, pilots completed two missions, during which they supervised a team of four CCAs, while flying their own next-generation fighter aircraft. Results suggested no differences in self-reported mental workload as a function of CCA asset similarity. Advantages of the approach are discussed along with future research opportunities.