Acquisition of Skill Sets and Mental Models Over Time

This paper is intended discuss issues associated with different measures of ability and performance with respect to the operation of multiple unmanned systems. In describing these differences, we are interested in illustrating general trends of skill acquisition and factors that may influence the rate at which skills are acquired over time. Based on the results across two experiments, we argue that declarative knowledge (e.g. target familiarity) represents a dimension performance that can improve over a short period of time (~2 hours), but other dimensions of performance (e.g. correct localization using ground images) represent difficult dimensions of performance that require long periods of time for significant improvement (+9 hours). Furthermore, individual differences, such as spatial ability, and reconnaissance performance appear to be associated with the rate at which operators improved at performing different types of localization tasks. Implications of these findings are discussed.