Current attentional guidance systems in Virtual Reality (VR) typically treat the user as a static receiver, employing fixed visual cues that fail to account for the fluctuating cognitive state. We address this limitation by positing that effective guidance requires modulating external stimuli to correspond with the brain’s intrinsic attentional sampling mechanism. Using EEG in an immersive environment, we provide direct evidence that this sampling periodicity is not fixed; specifically, the intensified sensorimotor integration load of hand-eye coordination drives the endogenous rhythm to decelerate from the alpha band (∼8 Hz) to the theta band (∼4 Hz). This neural adaptation dictates the temporal requirements for external guidance: identifying optimal parameters via Pareto optimization, we demonstrate that while a 4 Hz cue suffices for gaze, the computationally demanding coordination task requires a higher-frequency (7 Hz) cue to ensure sufficient temporal signal density. Validated in ecological VR scenarios, our adaptive strategy significantly enhanced interaction efficiency without increasing cognitive load. Beyond the specific implementation of flicker, this work establishes a critical design principle for next-generation attention-aware interfaces: maximizing performance by synchronizing information presentation with the user’s task-induced sensorimotor state.

Accurate 3D target selection in virtual reality (VR) is fundamentally impeded by pointing uncertainty along the depth axis, a challenge that existing 2D pointing models fail to address due to the complexities of depth perception. Near-eye interactions in VR are influenced by binocular depth cues and vergence-accommodation conflicts (VAC), which introduce significant depth perception biases that impair predictive performance. To address this issue, we first investigate these factors and derive a Gaussian distribution to model near-field depth biases within a 2.5m range. Second, to analyze pointing performance across this extended depth range, we classify 3D target motions into three distinct types: motion-indepth, motion-in-plane, and combined motion. Our analysis identifies that interaction depth and motion amplitude are the two most critical factors influencing pointing accuracy. Accordingly, by incorporating these factors alongside our perceptual bias Gaussian into the Ternary-Gaussian framework, we demonstrate significantly improved predictive performance across diverse 3D motion scenarios. These findings enhance the understanding of user perception in virtual environments and support the development of precise, context-aware interaction cues. Future research can extend these models to design real-time adaptive interfaces, thereby elevating user experiences in VR.

Virtual Reality (VR) technologies in fields such as telehealth, teleconferencing, and virtual education are significantly affected by end-to-end latency, which notably impacts users' interactive experience and performance. Previous research suggests that a perceptual threshold may exist-once latency is reduced below a certain level, users no longer perceive it, and their interactive performance remains largely unaffected. However, there is no consensus on the exact value of this absolute latency perception threshold. In this study, we employed an experimental design based on Fitts' law to investigate whether interaction strategies and task difficulty can alter the latency perception threshold (LPT), and how variations in this threshold influence users' interactive performance. The results show that the LPT is approximately 130-170 ms, and that when interaction strategies prioritize speed or when tasks become more challenging, users exhibit heightened sensitivity to latency. Due to the presence of the LPT, the effect of latency on interactive performance follows a nonlinear pattern, and building on this finding, we refined a Fitts' law model to incorporate the influence of latency. Notably, electroencephalogram (EEG) signals can still capture users' perception of latency when they are unaware of minor latency, demonstrating a level of sensitivity that exceeds conscious awareness. Our findings provide insights into latency effects on performance and perception, guiding the design of more responsive VR interaction systems.

Virtual Reality (VR) offers a valuable platform for real-life skills training. However, previous research has indicated that human’s perception of depth in VR differs from that of the real world. Such perceptual conflicts can impact immersion and the learning of skills, thus attracting widespread attention. Various methods have been proposed to enhance users’ depth perception, yet the underlying mechanisms of depth perception conflicts still require further research. In this paper, we used Error-Related Potentials (ErrPs) from electroencephalography (EEG) data to investigate the differences in participants’ perceptions at varying depths within the near-field. We designed a within-subjects experiment to successfully introduce depth perception conflicts. From participants exposed to three distinct depths, we collected questionnaire results, performance data, and EEG data. Our findings showed that EEG can effectively detect depth perception conflicts and, following each conflict, participants’ behavioral patterns showed significant changes. In situations with shallower depths, participants exhibited stronger responses to the designed conflicts. This increased sensitivity correlates with their accuracy in depth estimation. This study represents a novel approach to depth perception in VR using ErrPs, setting the stage for further use of physiological signals to measure the granularity of depth perception in VR/AR environments.