Sandra Malpica

Este trabajo presenta un análisis de la influencia de la carga cognitiva en las señales fisiológicas del usuario en realidad virtual (VR). Para ello, diseñamos un experimento multitarea en VR con distintos grados de carga cognitiva y movimiento. Nuestros resultados muestran la robustez de la actividad electrodérmica (EDA) como indicador de la carga cognitiva.

Virtual reality (VR) has the potential to become a revolutionary technology with a significant impact on our daily lives. The immersive experience provided by VR equipment, where the user’s body and senses are used to interact with the surrounding content, accompanied by the feeling of presence elicits a realistic behavioral response. In this work, we leverage the full control of audiovisual cues provided by VR to study an audiovisual suppression effect (ASE) where auditory stimuli degrade visual performance. In particular, we explore if barely audible sounds (in the range of the limits of hearing frequencies) generated following a specific spatiotemporal setup can still trigger the ASE while participants are experiencing high cognitive loads. A first study is carried out to find out how sound volume and frequency can impact this suppression effect, while the second study includes higher cognitive load scenarios closer to real applications. Our results show that the ASE is robust to variations in frequency, volume and cognitive load, achieving a reduction of visual perception with the proposed hardly audible sounds. Using such auditory cues means that this effect could be used in real applications, from entertaining to VR techniques like redirected walking.

Visual behavior depends on both bottom-up mechanisms, where gaze is driven by the visual conspicuity of the stimuli, and top-down mechanisms, guiding attention towards relevant areas based on the task or goal of the viewer. While this is well-known, visual attention models often focus on bottom-up mechanisms. Existing works have analyzed the effect of high-level cognitive tasks like memory or visual search on visual behavior; however, they haveoften done so with different stimuli, methodology, metrics and participants, which makes drawing conclusions and comparisons between tasks particularly difficult. In this work we present a systematic study of how different cognitive tasks affect visual behavior in a novel within-subjects design scheme. Participants performed free exploration, memory and visual search tasks in three different scenes while their eye and head movements were being recorded. We found significant, consistent differences between tasks in the distributions of fixations, saccades and head movements. Our findings can provide insights for practitioners and content creators designing task-oriented immersive applications.

Understanding human visual behavior within virtual reality environments is crucial to fully leverage their potential. While previous research has provided rich visual data from human observers, existing gaze datasets often suffer from the absence of multimodal stimuli. Moreover, no dataset has yet gathered eye gaze trajectories (i.e., scanpaths) for dynamic content with directional ambisonic sound, which is a critical aspect of sound perception by humans. To address this gap, we introduce D-SAV360, a dataset of 4,609 head and eye scanpaths for 360° videos with first-order ambisonics. This dataset enables a more comprehensive study of multimodal interaction on visual behavior in virtual reality environments. We analyze our collected scanpaths from a total of 87 participants viewing 85 different videos and show that various factors such as viewing mode, content type, and gender significantly impact eye movement statistics. We demonstrate the potential of D-SAV360 as a benchmarking resource for state-of-the-art attention prediction models and discuss its possible applications in further research. By providing a comprehensive dataset of eye movement data for dynamic, multimodal virtual environments, our work can facilitate future investigations of visual behavior and attention in virtual reality.

Time perception is fluid and affected by manipulations to visual inputs. Previous literature shows that changes to low-level visual properties alter time judgments at the millisecond-level. At longer intervals, in the span of seconds and minutes, high-level cognitive effects (e.g., emotions, memories) elicited by visual inputs affect time perception, but these effects are confounded with semantic information in these inputs, and are therefore challenging to measure and control. In this work, we investigate the effect of asemantic visual properties (pure visual features devoid of emotional or semantic value) on interval time perception. Our experiments were conducted with binary and production tasks in both conventional and head-mounted displays, testing the effects of four different visual features (spatial luminance contrast, temporal frequency, field of view, and visual complexity). Our results reveal a consistent pattern: larger visual changes all shorten perceived time in intervals of up to 3min, remarkably contrary to their effect on millisecond-level perception. Our findings may help alter participants’ time perception, which can have broad real-world implications.

Virtual reality (VR) is rapidly growing, with the potential to change the way we create and consume content. In VR, users integrate multimodal sensory information they receive to create a unified perception of the virtual world. In this survey, we review the body of work addressing multimodality in VR and its role and benefits in user experience, together with different applications that leverage multimodality in many disciplines. These works thus encompass several fields of research and demonstrate that multimodality plays a fundamental role in VR, enhancing the experience, improving overall performance, and yielding unprecedented abilities in skill and knowledge transfer.

We report an auditory effect of visual performance degradation in a virtual reality (VR) setting, where the viewing conditions are significantly different from previous studies. With the presentation of temporally congruent but spatially incongruent sound, we can degrade visual performance significantly at detection and recognition levels. We further show that this effect is robust to different types and locations of both auditory and visual stimuli. We also analyze participants behavior with an eye tracker to study the underlying cause of the degradation effect. We find that the performance degradation occurs even in the absence of saccades towards the sound source, during normal gaze behavior. This suggests that this effect is not caused by oculomotor phenomena, but rather by neural interactions or attentional shifts.

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.