Virtual reality (VR) often induces perceptual distortions, for instance, underestimates of perceived distance which may distort perceived size. However, other depth cues are available in these immersive environments that may help users perceive size accurately. Here, we examined how binocular and monocular depth cues, as well as object interaction, influenced the perceived size of 3D shapes in VR. The results showed that binocular cues play an essential role in size judgements, whereas dynamic monocular cues, such as motion parallax, did not fully compensate for their absence, underscoring the importance of rendering high-quality binocular cues for fine size discrimination tasks in virtual environments.

Virtual reality (VR) is distinguished by the rich, multimodal, im-mersive sensory information and affordances provided to the user. However, when moving about an immersive virtual world the vi-sual display often conflicts with other sensory cues due to design, the nature of the simulation, or to system limitations (for example impoverished vestibular motion cues during acceleration in racing games). Given that conflicts between sensory cues have been as-sociated with disorientation or discomfort, and theoretically could distort spatial perception, it is important that we understand how and when they are manifested in the user experience. To this end, this set of experiments investigates the impact of mismatch between physical and virtual motion parallax on the per-ception of the depth of an apparently perpendicular dihedral angle (a fold) and its distance. We applied gain distortions between visual and kinesthetic head motion during lateral sway movements and measured the effect of gain on depth, distance and lateral space compression. We found that under monocular viewing, observers made smaller object depth and distance settings especially when the gain was greater than 1. Estimates of target distance declined with increasing gain under monocular viewing. Similarly, mean set depth decreased with increasing gain under monocular viewing, except at 6.0 m. The effect of gain was minimal when observers viewed the stimulus binocularly. Further, binocular viewing (stereopsis) improved the precision but not necessarily the accuracy of gain perception. Overall, the lateral compression of space was similar in the stereoscopic and monocular test conditions. Taken together, our results show that the use of large presentation distances (at 6 m) combined with binocular cues to depth and distance enhanced humans' tolerance to visual and kinesthetic mismatch.

Alzheimer's disease (AD) is a genetically complex neurodegenerative disease, which leads to irreversible brain damage, severe cognitive problems and ultimately death. A number of clinical trials and study initiatives have been set up to investigate AD pathology, leading to large amounts of high dimensional heterogeneous data (biomarkers) for analysis. This paper focuses on combining clinical features from different modalities, including medical imaging, cerebrospinal fluid (CSF), etc., to diagnose AD and predict potential progression. Due to privacy and legal issues involved with clinical research, the study cohort (number of patients) is relatively small, compared to thousands of available biomarkers (predictors). We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information with limited training materials. Furthermore, we model (current and future) cognitive healthiness as a regression problem about age. By comparing the difference between predicted age and actual age, we manage to show statistical differences between different pathological stages. Verification tests are conducted based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Extensive comparison is made against different machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF), Decision Tree and Multilayer Perceptron (MLP). Experimental results show that our proposed algorithm achieves better results than the comparison targets, which indicates promising robustness for practical clinical implementation.