Cognitive Workload Quantified by Physiological Sensors in Realistic Immersive Settings

Abstract

AbstractCognitive workload changes have been studied and utilized as a means of assessment for engagement and learner’s performance during training. Yet, it is unclear how varying levels of simulator immersion affect learner cognitive workload. Wearable sensors allow us to monitor direct physiological changes associated with cognitive workload in real time. This study seeks to utilize multiple physiological and neurological measures: functional near-infrared spectroscopy (fNIRS), eye-tracking, electrodermal activity (EDA), heart rate, and respiratory rate; in order to assess cognitive workload changes during different training conditions. The National Aeronautics and Space Administration’s (NASA) Task Load Index (TLX) and flow state scale questionnaires were additionally used to record self-reported cognitive workload and subjective experience. Nine law enforcement trainees participated in different immersions conditions in a law enforcement use-of-force (UOF) simulator. Results from a low immersion condition were compared to results from a high immersion condition. Preliminary comparison between these two conditions suggests that the Index of Cognitive Activity (ICA) and respiration rate were greater in the low immersion condition. However, a notable increase in the oxygenated hemoglobin of the right anterior medial prefrontal cortex was detected via fNIRS. Heart rate also increased between the two conditions. Traditional questionnaires used to measure cognitive load showed no significance between conditions. Compared to self-report subjective metrics, biometrics such as fNIRS were operationally more effective at smaller sample sizes. Not only do these results show that features associated with trainees’ workload can viably be collected in realistic simulator settings, but they also suggest that increased immersion in law enforcement simulators may have a measurable effect on biometrics associated with cognitive workload.KeywordsHuman performanceTrainingCognitive workloadBiometricsfNIRSEye-trackingElectrodermal activityEDAHeart rateRespiratory rate