Investigating mental workload changes in a long duration supervisory control task

Abstract

With improving automation in many critical domains, operators will be expected to handle long periods of low task load while monitoring a system, and possibly responding to emergent situations. Monitoring the psychophysiological state of the operator during low task load may detect maladapted attention states in order to predict performance and facilitate a more effective workload transition during critical periods. This research explored the question of detecting anomalous attention states during transitions to high workload following extended periods of boredom using a non-invasive neuroimaging technique called functional near-infrared spectroscopy (fNIRS). Subjects at the point of lowest engagement and priming had a diminished hemodynamic response and performed worse on missile defense task, showing fNIRS may be useful for concurrent monitoring of the operator in such settings.