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There has been recent interest on the impact of emotional expressions of computers on people’s decision making. However, despite a growing body of empirical work, the mechanism underlying such effects is still not clearly understood. To address this issue the paper explores two kinds of processes studied by emotion theorists in human–human interaction: inferential processes, whereby people retrieve information from emotion expressions about other’s beliefs, desires, and intentions; affective processes, whereby emotion expressions evoke emotions in others, which then influence their decisions. To tease apart these two processes as they occur in human–computer interaction, we looked at physiological measures (electrodermal activity and heart rate deceleration). We present two experiments where participants engaged in social dilemmas with embodied agents that expressed emotion. Our results show, first, that people’s decisions were influenced by affective and cognitive processes and, according to the prevailing process, people behaved differently and formed contrasting subjective ratings of the agents; second we show that an individual trait known as electrodermal lability, which measures people’s physiological sensitivity, predicted the extent to which affective or inferential processes dominated the interaction. We discuss implications for the design of embodied agents and decision making systems that use emotion expression to enhance interaction between humans and computers. [ABSTRACT FROM AUTHOR]

A critical yet unresolved challenge in designing space-adaptive narratives for Augmented Reality (AR) is to provide consistently immersive user experiences anywhere, regardless of physical features specific to a space. For this, we present a comprehensive analysis on a series of user studies investigating how the size, density, and layout of real indoor spaces affect users playing Fragments, a space-adaptive AR detective game. Based on the studies, we assert that moderate levels of traversability and visual complexity afforded in counteracting combinations of size and complexity are beneficial for narrative experience. To confirm our argument, we combined the experimental data of the studies (n=112) to compare how five different spatial complexity conditions impact narrative experience when applied to contrasting room sizes. Results show that whereas factors of narrative experience are rated significantly higher in relatively simple settings for a small space, they are less affected by complexity in a large space. Ultimately, we establish guidelines on the design and placement of space-adaptive augmentations in location-independent AR narratives to compensate for the lack or excess of affordances in various real spaces and enhance user experiences therein.

Despite the importance of avatar representation on user experience for Mixed Reality (MR) remote collaboration involving various device environments and large amounts of task-related information, studies on how controlling visual parameters for avatars can benefit users in such situations have been scarce. Thus, we conducted a user study comparing the effects of three avatars with different transparency levels (Nontransparent, Semi-transparent, and Near-transparent) on social presence for users in Augmented Reality (AR) and Virtual Reality (VR) during task-centric MR remote collaboration. Results show that avatars with a strong visual presence are not required in situations where accomplishing the collaborative task is prioritized over social interaction. However, AR users preferred more vivid avatars than VR users. Based on our findings, we suggest guidelines on how different levels of avatar transparency should be applied based on the context of the task and device type for MR remote collaboration.

In this paper, we present a prototype system for sharing a user's hand force in mixed reality (MR) remote collaboration on physical tasks, where hand force is estimated using wearable surface electromyography (sEMG) sensor. In a remote collaboration between a worker and an expert, hand activity plays a crucial role. However, the force exerted by the worker's hand has not been extensively investigated. Our sEMG-based system reliably captures the worker's hand force during physical tasks and conveys this information to the expert through hand force visualization, overlaid on the worker's view or on the worker's avatar. A user study was conducted to evaluate the impact of visualizing a worker's hand force on collaboration, employing three distinct visualization methods across two view modes. Our findings demonstrate that sensing and sharing hand force in MR remote collaboration improves the expert's awareness of the worker's task, significantly enhances the expert's perception of the collaborator's hand force and the weight of the interacting object, and promotes a heightened sense of social presence for the expert. Based on the findings, we provide design implications for future mixed reality remote collaboration systems that incorporate hand force sensing and visualization.

There have been attempts to provide new cinematic experiences by connecting TV or movie content to suitable locations through augmented reality (AR). However, few studies have suggested a method to manage breakdowns in continuity due to spatial transitions. Thus, we propose a method to manage the spatial transition that occurs when we create a TV show trajectory by mapping TV show scenes with spatiotemporal information to the real world. Our approach involved two steps. The first step is to reduce the spatial transition considering the sequence, location, and importance of TV show scenes when creating the TV show trajectory in the authoring tool. The second is to fill the spatial transition with additional TV show scenes considering sequence, importance, and user interest when providing the TV show trajectory in the mobile application. The user study results showed that reducing spatial transition increases narrative engagement by allowing participants to see important content within the trajectory. The additional content in spatial transition decreased the physical demand and effort in terms of the perceived workload, although it increased the task completion time. Integrated spatial transition management improved the overall cinematic augmented reality (CAR) experience of the TV show. Furthermore, we suggest design implications for realizing the CAR of TV shows based on our findings., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.)

This paper proposes a novel panoramic texture mapping-based rendering system for real-time, photorealistic reproduction of large-scale urban scenes at a street level. Various image-based rendering (IBR) methods have recently been employed to synthesize high-quality novel views, although they require an excessive number of adjacent input images or detailed geometry just to render local views. While the development of global data, such as Google Street View, has accelerated interactive IBR techniques for urban scenes, such methods have hardly been aimed at high-quality street-level rendering. To provide users with free walk-through experiences in global urban streets, our system effectively covers large-scale scenes by using sparsely sampled panoramic street-view images and simplified scene models, which are easily obtainable from open databases. Our key concept is to extract semantic information from the given street-view images and to deploy it in proper intermediate steps of the suggested pipeline, which results in enhanced rendering accuracy and performance time. Furthermore, our method supports real-time semantic 3D inpainting to handle occluded and untextured areas, which appear often when the user's viewpoint dynamically changes. Experimental results validate the effectiveness of this method in comparison with the state-of-the-art approaches. We also present real-time demos in various urban streets.