Proceedings of the Annual Meeting of the Cognitive Science Society

Cybersickness is a major barrier to the widespread adoption of virtual reality, arising from neural dissonance between visual and vestibular stimuli. Although kinematics-based deep learning enables non-intrusive detection, existing models often lack neurophysiological grounding and fail to capture dynamic sensory recalibration and continuous symptom accumulation. To address these limitations, we propose the Neural Sensory Conflict Network (NSCNet), a biologically inspired framework grounded in Sensory Conflict Theory. Specifically, NSCNet incorporates a Conflict Alignment Embedding to model the integration of discordant sensory inputs, a State-Space Re-entrant Experts module that combines selective state-space modeling with Re-entrant Experts to capture the temporal dynamics of neural dissonance and functional modularity, and a Dynamic Sensory Reweighting mechanism that approximates adaptive gain control in the central nervous system. Experiments on the MSCVR and VR Cybersickness datasets demonstrate state-of-the-art performance, suggesting that explicitly modeling neurocognitive mechanisms improves the predictive fidelity of cybersickness detection.