  High-risk operational tasks such as those in aviation require training
environments that are realistic and capable of inducing high levels of
workload. Virtual Reality (VR) offers a simulated 3D environment for immersive,
safe and valid training of pilots. An added advantage of such training
environments is that they can be personalized to enhance learning, e.g., by
adapting the simulation to the user's workload in real-time. The question
remains how to reliably and robustly measure a pilot's workload during the
training. In this study, six novice military pilots (average of 34.33 flight
hours) conducted a speed change maneuver in a VR flight simulator. In half of
the runs an auditory 2-back task was added as a secondary task. This led to
trials of low and high workload which we compared using the pilot's control
input in longitudinal (i.e., pitch) and lateral (i.e., roll) directions. We
extracted Pilot Inceptor Workload (PIW) from the stick data and conducted a
binary logistic regression to determine whether PIW is predictive of
task-induced workload. The results show that inputs on the stick along its
longitudinal direction were predictive of workload (low vs. high) when
performing a speed change maneuver. Given that PIW may be a task-specific
measure, future work may consider (neuro)physiological predictors. Nonetheless,
the current paper provides evidence that measuring PIW in a VR flight simulator
yields real-time and non-invasive means to determine workload.
