Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces - Hinss, Marcel F.; Brock, Anke M.; Roy, Raphaëlle N. (Frontiers in Neuroergonomics, 2022)
Operators of complex systems across multiple domains (e.g. aviation, automotive and nuclear power industry) are required to perform their tasks over prolonged and continuous periods of time. Mental fatigue as well as reduced cognitive flexibility, attention and situational awareness all result from prolonged continuous use, putting at risk the safety and efficiency of complex operations. Mental state-based adaptive systems may be a solution to this problem. These systems infer the current mental state of an operator based on a selection of metrics ranging from operator independent measures (e.g. weather and time of day), to behavioral (e.g. reaction time and lane deviation) as well as physiological markers (e.g. electroencephalography and cardiac activity). The interaction between operator and system may then be adapted in one of many ways to mitigate any detected degraded cognitive state, thereby ensuring continued safety and efficiency. Depending on the task at hand and its specific problems, possible adaptations -usually based on machine learning estimations- for instance include modifications of information, presentation modality or stimuli salience, as well as task scheduling. Research on adaptive systems is at the interface of several domains, including neuroergonomics, human factors and human-computer interaction in an applied and ecological context, necessitating careful consideration of each of the aforementioned aspects. This paper provides an overview of some of the key questions and aspects to be considered by researchers for the design of mental state-based adaptive systems, while also promoting their application during prolonged continuous use to pave the way towards safer and more efficient human-machine interaction.