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.

With the advancement of virtual reality (VR), human-robot interaction within remote collaboration contexts has emerged as a pivotal research area. However, the nuanced interplay between the user's perspective and the robot's level of automation, and its subsequent impact on the operator's sense of agency (SoA), remains underexplored. This study investigates how perspective and collaboration mode-differentiated by the robot's level of automation-affect the SoA in VR-based human-robot collaboration. We implemented a 2 (perspective: first-person view[FPV] vs. third-person view[TPV]) × 3 (collaboration mode: Direct Remote Control, Supervisory Cooperation, Spectator Mode) experimental design. SoA was assessed via a multimodal approach, measuring both the explicit judgement of agency (JoA) and the implicit feeling of agency (FoA). The results revealed a dissociation between these two components: explicit JoA was dictated by the collaboration mode, with active modes yielding a higher explicit JoA than the passive Spectator Mode. In contrast, implicit FoA exhibited a significant interaction effect. Under FPV, the influence of collaboration mode was pronounced, where Direct Remote Control elicited the strongest FoA; however, under TPV, the differences among collaboration modes disappeared. The relationship between cybersickness and SoA was also explored. These findings suggest that SoA is not a unitary concept but a multifaceted experience, co-shaped by the user's perspective and their role as either a decision-maker or an executor. Furthermore, we investigate the relationship between cybersickness and SoA. Our study offers novel insights for the design of VR-based human-robot systems.

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.