Tracking moving objects is a critical skill for many everyday tasks, such as crossing a busy street, driving a car or catching a ball. Attention is a key cognitive function that supports object tracking; however, our understanding of the brain mechanisms that support attention is almost exclusively based on evidence from tasks that present stable objects at fixed locations. Accounts of multiple object tracking are also limited because they are largely based on behavioral data alone and involve tracking objects in a 2D plane. Consequently, the neural mechanisms that enable moment-by-moment tracking of goal-relevant objects remain poorly understood. To address this knowledge gap, we developed SABER (Spatial Attention, Brain, Extended Reality), a new framework for studying the behavioral and neural dynamics of attention to objects moving in 3D. Participants (n=32) completed variants of a task inspired by the popular virtual reality (VR) game Beat Saber, where they used virtual sabers to strike stationary and moving color-defined target spheres while we recorded electroencephalography (EEG). We first established that standard univariate EEG metrics which are typically used to study spatial attention to static objects presented on 2D screens, can generalize effectively to an immersive VR context involving both static and dynamic 3D stimuli. We then used a computational modeling approach to reconstruct moment-by-moment attention to the locations of stationary and moving objects from oscillatory brain activity, demonstrating the feasibility of precisely tracking attention in a 3D space. These results validate SABER, and provide a foundation for future research that is critical not only for understanding how attention works in the physical world, but is also directly relevant to the development of better VR applications. The insights gained here can potentially inform the design of more intuitive interfaces, effective training simulations, and immersive experiences optimized for the human attention system.

When several individuals collaborate on a shared task, their brain activities often synchronize. This phenomenon, known as Inter-brain Synchronization (IBS), is notable for inducing prosocial outcomes such as enhanced interpersonal feelings, including closeness, trust, empathy, and more. Further strengthening the IBS with the aid of external feedback would be beneficial for scenarios where those prosocial feelings play a vital role in interpersonal communication, such as rehabilitation between a therapist and a patient, motor skill learning between a teacher and a student, and group performance art. This paper investigates whether visual, auditory, and haptic feedback of the IBS level can further enhance its intensity, offering design recommendations for feedback systems in IBS. We report findings when three different types of feedback were provided: IBS level feedback by means of on-body projection mapping, sonification using chords, and vibration bands attached to the wrist.

Augmented reality is projected to be a primary mode of information consumption on the go, seamlessly integrating virtual content into the physical world. However, the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety, and the implications of these demands are not well understood. Here, we investigate the impact of virtual path guidance and augmentation density (visual clutter) on search performance and memory. Participants walked along a predefined path, searching for physical or virtual items. They experienced two levels of augmentation density, and either walked freely or with enforced speed and path guidance. Augmentation density impacted behavior and reduced awareness of uncommon objects in the environment. Analysis of search task performance and post-experiment item recall revealed differing attention to physical and virtual objects. On the basis of these findings we outline considerations for AR apps designed for use on the go.

We study attention in mobile Augmented Reality (AR) using object recall as a proxy outcome. We observe that the ability to recall an object (physical or virtual) that was encountered in a mobile AR experience depends on many possible impact factors and attributes, with some objects being readily recalled while others are not, and some people recalling objects overall much better or worse than others. This opens up a potential cognitive attack in which adversaries might create conditions that make an AR user not recall certain potentially mission-critical objects. We explore whether a calibrated predictor of object recall can help shield against such cognitive attacks. We pool data from four mobile AR studies (with a total of 1,152 object recall probes) and fit a Partial Least Squares Structural Equation Model (PLS-SEM) with formative Object, Scene, and User State composites predicting recall, also benchmarking against Random Forest and multilayer perceptron classifiers. PLS-SEM attains the best F1 score in three of four studies. Additionally, path estimates identify lighting, augmentation density, AR registration stability, cognitive load, and AR familiarity as primary drivers. The model outputs per-object recall probabilities that can drive interface adjustments when predicted recall falls. Overall, PLS-SEM provides competitive accuracy with interpretable levers for design and evaluation in mobile AR.

Audience reactions can considerably enhance live experiences; conversely, in anytime/anywhere augmented reality (AR) experiences, large crowds of people might not always be available to congregate. To get closer to simulating live events with large audiences, we created a mobile AR experience where users can wander around naturally and engage in AR theater with virtual audiences trained from real audiences using imitation learning. This allows us to carefully capture the essence of human imperfections and behavior in artificial intelligence (AI) audiences. The result is a novel mobile AR experience in which solitary AR users experience an augmented performance in a physical space with a virtual audience. Virtual dancers emerge from the surroundings, accompanied by a digitally simulated audience, to provide a community experience akin to immersive theater. In a pilot study, simulated human avatars were vastly preferred over just audience audio commentary. We subsequently engaged 20 participants as attendees of an AR dance performance, comparing a no-audience condition with a simulated audience of six onlookers. Through questionnaires and experience reports, we investigated user reactions and behavior. Our results demonstrate that the presence of virtual audience members caused attendees to perceive the performance as a social experience with increased interest and involvement in the event. On the other hand, for some attendees, the dance performances without the virtual audience evoked a stronger positive sentiment.

Head-worn augmented reality (AR) is a hotly pursued and increasingly feasible contender paradigm for replacing or complementing smartphones and watches for continual information consumption. Here, we compare three different AR navigation aids (on-screen compass, on-screen radar and in-world vertical arrows) in a wide-area outdoor user study (n=24) where participants search for hidden virtual target items amongst physical and virtual objects. We analyzed participants’ search task performance, movements, eye-gaze, survey responses and object recall. There were two key findings. First, all navigational aids enhanced search performance relative to a control condition, with some benefit and strongest user preference for in-world arrows. Second, users recalled fewer physical objects than virtual objects in the environment, suggesting reduced awareness of the physical environment. Together, these findings suggest that while navigational aids presented in AR can enhance search task performance, users may pay less attention to the physical environment, which could have undesirable side-effects.

Reality Distortion Room (RDR) is a proof-of-concept augmented reality system using projection mapping and unencumbered interaction with the Microsoft RoomAlive system to study a user’s locomotive response to visual effects that seemingly transform the physical room the user is in. This study presents five effects that augment the appearance of a physical room to subtly encourage user motion. Our experiment demonstrates users’ reactions to the different distortion and augmentation effects in a standard living room, with the distortion effects projected as wall grids, furniture holograms, and small particles in the air. The augmented living room can give the impression of becoming elongated, wrapped, shifted, elevated, and enlarged. The study results support the implementation of AR experiences in limited physical spaces by providing an initial understanding of how users can be subtly encouraged to move throughout a room.

Dynamic Theater explores the use of augmented reality (AR) in immersive theater as a platform for digital dance performances. The project presents a locomotion-based experience that allows for full spatial exploration. A large indoor AR theater space was designed to allow users to freely explore the augmented environment. The curated wide-area experience employs various guidance mechanisms to direct users to the main content zones. Results from our 20-person user study show how users experience the performance piece while using a guidance system. The importance of stage layout, guidance system, and dancer placement in immersive theater experiences are highlighted as they cater to user preferences while enhancing the overall reception of digital content in wide-area AR. Observations after working with dancers and choreographers, as well as their experience and feedback are also discussed.

By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these environments. Here, we conducted a wide-area outdoor AR user study ($n=48$) using a commercially available AR headset (Microsoft Hololens 2) to compare (1) user interactions with physical and virtual objects in the environment (2) the effects of different lighting conditions on user behavior and AR experience and (3) the impact of varying cognitive load on AR task performance. Participants engaged in a treasure hunt task where they searched for and classified virtual target items (green "gems") in an augmented outdoor courtyard scene populated with physical and virtual objects. Cognitive load was manipulated so that in half the search trials users were required to monitor an audio stream and respond to specific target sounds. Walking paths, head orientation and eye gaze information were measured, and users were queried about their memory of encountered objects and provided feedback on the experience. Key findings included (1) Participants self-reported significantly lower comfort in the ambient natural light condition, with virtual objects more visible and participants more likely to walk into physical objects at night; (2) recall for physical objects was worse than for virtual objects, (3) participants discovered more gems hidden behind virtual objects than physical objects, implying higher attention on virtual objects and (4) dual-tasking modified search behavior. These results suggest there are important technical, perceptual and cognitive factors that must be considered if the full potential of "anywhere and anytime mobile AR" is to be realized.

Narrative is an essential factor that makes games more enjoyable. However, predicting the story quality has been challenging for decades. In this paper, we propose to use contextual word embedding models such as BERT and ELMo, for predicting a story's success in terms of quality and popularity by using the story text only. Since deep learning models generally require extensive data, we conducted experiments to test the efficacy of our proposed model by leveraging the movie plot summaries. We present the results of the evaluations and conclude with discussions.