Sensitivity of candidate markers of psychophysiological strain to cyclical changes in manual control load during simulated process control

Abstract

Complex systems are vulnerable to unpredictable breakdowns in operator performance. Although primary task goals are typically protected by compensatory effort, such protection may break down under fatigue and high strain. Detection of strain states would enable prediction of increased operational risk through adaptive automation, triggering a switch of control from human to computer. A simulated process control task was used to identify markers of strain under a cyclic loading procedure, which forced performance breakdown through stepwise changes in control load. Four trained participants provided data on control performance and a range of candidate psychophysiological markers of strain (two EEG power ratios and HRV). Within-individual analyses showed the strongest sensitivity for ‘task load index’ (TLI), an EEG measure based on executive control activity in frontal brain areas, though all measures were sensitive for some participants. The implications of such findings for the development of a closed loop system for adaptive automation are discussed.