Virtual reality has been effectively used for eliciting emotions, yet most research focuses on the intensity of affective responses rather than on how interaction influences those experiences. To address this gap, we advance a validated VR emotion-elicitation dataset through two key extensions. First, we add a new high-arousal, high-valence scene and validate its effectiveness in a within-subject study (N=24). Second, we incorporate interactive elements into each scene, creating both interactive and non-interactive versions to examine the impact of interaction on emotional responses. We evaluate interaction through a multimodal approach combining subjective ratings and physiological signals to capture both conscious and unconscious affective responses. Our evaluation study (N=84) shows that interaction not only amplifies emotions but modulates them in context, supporting coping in negative scenes and enhancing enjoyment in positive scenes. These findings highlight the potential of scene-tailored interaction for different applications, where regulating emotions is as important as eliciting them.

Visual search is a core component of mixed reality (MR) interactions, influenced by the complexities of MR application contexts. In this paper, we investigate how prevalent factors in MR influence visual search performance and spatial regularity memory -- including the physical environment complexity, secondary task presence, virtual content depth and spatial layout configurations. Contrary to prior work, we found that the secondary auditory task did not have a significant main effect on visual search performance, while significantly elevating higher perceived workload measures in all conditions. Complex environments and varied virtual elements depths significantly hinder visual search, but did not significantly increase perceived workload measures. Finally, participants did not explicitly recognize repeated spatial configurations of virtual elements, but performed significantly better when searching repeated spatial configurations, suggesting implicit memory of spatial regularities. Our work presents novel insights on visual search and highlights key considerations when designing MR for different application contexts.

Figure 1: The consumed endurance at 60 seconds.(1) attaching 1 kg weight boosts arm exertion to 80%; (2) bare-hand only consumed 20%.A more intense red coloration on the shoulder indicates a higher level of fatigue experienced by the user.

Social virtual reality (VR) is the experience of a shared virtual space populated by virtual representations of each individual, allowing them to communicate, collaborate and interact with each other not unlike the real world. Conventional avatars generally mirror factors like an individual’s appearance, speech, movement, and so on, yet a VR can offer many more possibilities to represent a person beyond what reality can offer. One such representation is that of emotions and empathy. To that regard, we propose Bioresponsive Avatars, an avatar system that predicts user emotional states and represents them visually via their avatar appearance. To achieve this, we first conducted an avatar design workshop to understand how user’s imagine emotional states to appear on an avatar. Then, we performed an in-the-wild demonstration of a social VR prototype where dyadic users are presented with affective topics to communicate while their avatars adapt based on their predicted emotions. • Developed bioresponsive avatars to map emotions into Valence–Arousal model. • Implemented a real-time avatar using physiological sensing and ML in social VR. • Conducted an in-the-wild study with 55 participants, noting patterns of empathy in VR.