Real-time performance modelling of a Sustained Attention to Response Task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participants' reaction times during a monotonous task. A laboratory-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Relevant parameters are then used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models is compared to detect in real time - minute by minute - the lapses in vigilance during the task. It is shown that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables the detection of vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared with neural networks and generalised linear mixed models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks. Statement of Relevance: Existing research on monotony is largely entangled with endogenous factors such as sleep deprivation, fatigue and circadian rhythm. This paper uses a Bayesian model to assess the effects of a monotonous task on vigilance in real time. It is shown that the negative effects of monotony on the ability to sustain attention can be mathematically modelled and predicted in real time using surrogate measures, such as reaction times. This allows the modelling of vigilance fluctuations.