Neural Efficiency Metrics in Neuroergonomics: Theory and Applications

Abstract The finite nature of human cognitive capacity demands an efficient relationship between invested mental effort and task performance. As a result, the process of learning can be partially viewed as the acquisition of knowledge and formulation of strategies that attempt to reduce relative cognitive demands and increase overall mental efficiency, thereby allowing better performance. The demands of metabolically expensive neuronal activity involved in the maintenance of active cognitive resources imply that changes in mental efficiency are coupled with changes in neural efficiency (NE). Hybridization of task performance and cognitive load measures in the form of NE metrics may not only be more sensitive to changes in experimental conditions and training paradigms, but also simultaneously provide useful interpretations that can be utilized to optimize training, user interface, and environmental design. In this paper, we discuss the concept, applications, and challenges of collecting NE measurements along with current neuroimaging techniques and methodologies employed.