Zeitgebers-Based User Time Perception Analysis and Data-Driven Modeling via Transformer in VR

TL;DR

This paper explores how environmental cues and tasks influence time perception in VR, introducing a neural network model that predicts subjective time changes and their impact on user experience.

Contribution

It presents a novel data-driven model combining CNN and Transformer architectures to analyze and predict time perception in VR based on multimodal data.

Findings

Task factors significantly affect time perception.

Red light and slow music lead to time underestimation.

Time underestimation correlates with better user experience.

Abstract

Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can manipulate users' sense of space and time. While the sensation of "losing track of time" is often associated with enjoyable experiences, the link between time perception and user experience in VR and its underlying mechanisms remains largely unexplored. This study investigates how different zeitgebers-light color, music tempo, and task factor-influence time perception. We introduced the Relative Subjective Time Change (RSTC) method to explore the relationship between time perception and user experience. Additionally, we applied a data-driven approach called the Time Perception Modeling Network (TPM-Net), which integrates Convolutional Neural Network (CNN) and Transformer architectures to model time perception based on multimodal physiological and zeitgebers data. With 56 participants in a between-subject experiment, our results show that task factors significantly influence time perception, with red light and slow-tempo music further contributing to time underestimation. The RSTC method reveals that underestimating time in VR is strongly associated with improved user experience, presence, and engagement. Furthermore, TPM-Net shows potential for modeling time perception in VR, enabling inference of relative changes in users' time perception and corresponding changes in user experience. This study provides insights into the relationship between time perception and user experience in VR, with applications in VR-based therapy and specialized training.