The study of cognitive load (CL) has been an active field of research across disciplines such as psychology, education, and computer graphics and visualization for decades. In the context of Virtual Reality (VR), understanding mental demand becomes particularly relevant, as immersive experiences increasingly integrate multisensory stimuli that require users to distribute their limited cognitive resources. In this work, we investigate the effects of cognitive load during a search task in VR, combining objective and subjective measurements, including physiological signals and validated questionnaires. We designed an experiment in which participants performed a visual search task under two cognitive load conditions (either alone or while responding to a concurrent auditory task) and across two visual search areas (90° and 360°). We collected a rich dataset comprising task performance, eye tracking, electrocardiogram (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and inertial measurements, along with subjective assessments (NASA-TLX questionnaires). Our analysis shows that increased cognitive load hinders visual search performance and affects multiple physiological markers, offering a solid foundation for future research on cognitive load in multisensory virtual environments.

Abstract 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.

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.

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 have often 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.

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.