Data-Driven Spatio-Temporal Analysis via Multi-Modal Zeitgebers and Cognitive Load in VR

Virtual Reality (VR) produces a highly realistic simulation environment to engage users with Immersive Virtual Environments (IVEs). To interact effectively with users, VR builds intensive media through the multi-modal sense functions in the lower level, such as visual, auditory, tactile, and olfactory senses. However, the higher-level perceptions, e.g., the temporal duration, the sense of presence, and the cognitive load are less explored. These higher-level perceptions are part of the critical evaluation criteria for VR design. In this paper, we divide the external zeitgebers into visual and auditory zeitgebers. We then combine these zeitgebers with the attention-oriented cognitive load to investigate their effects on temporal estimation and presence, particularly in IVEs. We propose a data-driven method to build a multi-modal predictive equation for time estimation and presence, in an effort to figure out the essential elements of users' spatial and temporal perception in VR. We also design a complicated application and validate the predictive equation. Our feature-based model is able to guide the VR application design in terms of the subjective time length judgment and presence of users as well as achieve a better VR user experience.