Characterization of Mental Models in a Virtual Reality-Based Multitasking Scenario

Abstract

This research developed an empirical approach for characterizing user mental models in multitasking scenarios in virtual reality (VR). Participants were required to attend to and detect different types of perceptual events occurring randomly in time in a virtual locomotion environment (VLE) simulation while carrying on physical activity (walking on a treadmill). Three different forms of mental models for performing the perceptual task were hypothesized and expected response patterns for situation awareness (SA), task performance measures and mental workload ratings were compared with observed data. Results demonstrated the SA and task measures to be useful for identifying the occurrence of different mental models and to reveal response patterns in-line with hypotheses. However, in the multitasking scenario, the process of developing SA appeared to be substantially influenced by the physical and cognitive task demands; thus, the progressive development of highly accurate mental models of event distributions appeared to be restricted. Possible applications of the results of this study include development of training programs for mobile, visual inspection tasks, such as airport roving security patrols, for accurate mental model development and promoting detection of critical events.