Investigating mental workload changes in a long duration supervisory control task

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.

Duke Scholars

Author Mary "Missy" Cummings Electrical and Computer Engineering