Cognitive load plays an important role during learning and working, as it has been linked to wellfunctioning cognitive processes, performance, burnout and depression. Nonetheless, attempts to assess cognitive load in real-time by means of physiological data have been proven difficult, and interpreting these data remains challenging.The aim of this study is to examine whether and how well experienced cognitive load can be measured through psychophysiological data. The approach of this study is rather unique, for a combination of reasons. First, this study takes a multimodal approach, monitoring EDA (electrodermal activity), EEG (electroencephalography) and EOG (electrooculography). Second, this study is based on a relatively intensive data collection (N = 46) in a controlled lab setting in which varying cognitive load levels are deliberately induced. Finally, not only focussing on statistical significance, but also on the size of the association gives insights into how suitable physiological markers are to measure cognitive load. Results from a multilevel analysis suggest that the following physiological markers might be related to cognitive load, for example, in an industrial context: the rate and the duration of skin conductance responses, the alpha power, the alpha peak frequency and the eye blink rate. About 22.8% of the variance in self-reported cognitive load can be explained using these five measures.
Operators in closed-circuit television (CCTV) control rooms have to monitor large sets of video feeds coming from an ever increasing number of cameras. To assist these operators in their demanding day-to-day tasks, AI-driven support systems accompanied by user-centric interfaces are being developed. However, prototyping these support systems and testing them in operative control rooms can be a challenge. Therefore, in this paper, we present a virtual reality (VR) control room which can be used to investigate the effects of existing and future support systems on operators' performance and behaviour in a fully controlled environment. Important assets of this VR control room include the possibility to subject operators to different levels of cognitive load and to monitor their cognitive-affective states using not only subjective but also behavioural and physiological techniques.
Self-monitoring is considered a promising tool for self-management in clinical mental health, such as for coping with excessive stress. Detecting debilitating stress before the onset of a psychopathology is becoming more of interest both for practitioners and the scientific community. However, the development of mental well-being technology focusing on stress is disrupted by the complexity of accurately measuring stress, as no clear idea exists on the construct and how it should be measured. There is also limited knowledge on the perception of perceived quality of the outcomes from a stress algorithm and the variety in its behavioural consequences. Therefore, the purpose of this study is to explore the impact of such digital self-monitoring technology for stress. It applies a qualitative method, by using semi-structured interviews. The most important resulting themes to users of this application were data-interpretation and a request for transparency. Results indicated that the majority of the predictions of the stress algorithm were not in line with the expectations of the users. The implications of these findings reveal how stress algorithms can make participants doubt their own self judgment on assessing their daily stress levels.
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