Comparing Three Variants of a Computational Process Model of Basic Aircraft Maneuvering

A key objective of cognitive modeling research at the Air Force Research Laboratory’s Warfighter Training Research Division is to be able to explore the effects of background knowledge and task strategies on performance and learning of skills relevant to accomplishing the Air Force mission. In pursuit of that objective, this paper compares three variants of a computational process model of basic aircraft maneuvering. All three variants are embodied performance models implemented in the ACT-R cognitive modeling architecture. The model variants operate a Predator UAV Synthetic Task Environment (STE). Each model variant implements a different combination of background knowledge and task strategy for completing the basic maneuvering task. The three variants of the model are called Model Variant P (Performance only), Model Variant CP (Control and Performance) and Model Variant CFP (Control Focus and Performance). Model Variant P lacks the knowledge of control instrument settings typically available to expert pilots and only considers performance indicators in completing the basic maneuvering task. Model Variant CP has knowledge of control instrument settings needed to accomplish various performance objectives and uses that knowledge as part of a crosscheck strategy which includes attending equally to control and performance indicators. Model Variant CFP also has knowledge of control instrument settings, but has a different crosscheck strategy which includes focusing on control instruments until they are correctly set, in addition to normal crosschecking across control and performance indicators. This paper documents efforts to use these model variants to explore the relative effects of differences in knowledge and task strategy on pilot performance in UAV basic maneuvering.