Anticipating human error before it happens: Towards a psychophysiological model for online prediction of mental workload

Abstract

Mental workload is a key factor influencing the occurrence of human error; specifically in remotely-operated vehicle operations. Both low and high mental workload has been found to disrupt performance in a nonlinear fashion at a given task; however, research that has attempted to predict individual mental workload has met with little success. The objective of the present study is to investigate the potential of the dual-task paradigm and prefrontal cortex oxygenation as online measures of mental workload. Subjects performed a computerized object tracking task in which they had to follow a dynamic target with their aircraft. Task difficulty was manipulated in terms of processing load and difficulty of control: two critical sources of workload associated with remotely operating a vehicle. Mental workload was assessed by a secondary concurrent time production task and a functional near infrared spectrometer. Results show that the effects of task difficulty differ across measures of mental workload. This pattern of behavioural and neurophysiologic results suggests that the empirically-based selection of an appropriate secondary task for the measure of mental workload is critical as its sensitivity may vary considerably depending on task factors.