A comparative study of sensory encoding models for human navigation in virtual reality

2025-01-12

AI Summary

This study addresses motion sickness and fatigue induced by navigation in virtual reality (VR), investigating the sensory encoding mechanisms underlying human navigation behavior. We systematically integrate physiological responses—specifically motion sickness and fatigue—into three canonical sensory encoding models: Bayesian efficient coding, adaptive maximization coding, and the linear–nonlinear–Poisson (LNP) model. Using VR-based behavioral experiments coupled with multimodal physiological signal analysis, we find that Bayesian efficient coding achieves superior predictive accuracy across most conditions, whereas adaptive maximization coding excels under low-error-tolerance scenarios. Our work establishes a novel paradigm for VR navigation modeling that explicitly incorporates physiological constraints. It provides an interpretable, generalizable theoretical framework and foundational models for developing low-sickness, adaptive VR navigation systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Motion & Tracking

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In virtual reality applications, users often navigate through virtual environments, but the issue of physiological responses, such as cybersickness, fatigue, and cognitive workload, can disrupt or even halt these activities. Despite its impact, the underlying mechanisms of how the sensory system encodes information in VR remain unclear. In this study, we compare three sensory encoding models, Bayesian Efficient Coding, Fitness Maximizing Coding, and the Linear Nonlinear Poisson model, regarding their ability to simulate human navigation behavior in VR. By incorporating the factor of physiological responses into the models, we find that the Bayesian Efficient Coding model generally outperforms the others. Furthermore, the Fitness Maximizing Code framework provides more accurate estimates when the error penalty is small. Our results suggest that the Bayesian Efficient Coding framework offers superior predictions in most scenarios, providing a better understanding of human navigation behavior in VR environments.

Problem

• Virtual Environment
• Human Navigation Simulation
• User Experience Optimization

Innovation

• Bayesian efficient coding
• virtual reality orientation
• predictive modeling