Cultural differences influence how cyclists and drivers interact, affecting global autonomous vehicle (AV) adoption. AV-cyclist interfaces are needed to clarify AV intentions and resolve ambiguities when no human driver is present. These must adapt across cultures and road infrastructure. We conducted the first cross-cultural AV-cyclist user study across Stockholm (high segregation of cyclists from drivers), Glasgow (some segregation), and Muscat (no segregation). Cyclists used an AR simulator to cycle in physical space and experienced three holistic AV-cyclist interfaces. These integrated multiple interfaces into a larger ecosystem, e.g., a smartwatch synchronised with on-vehicle eHMI. Interfaces communicated AV location, intentions, or both. Riders from all cities preferred combined AV location and intention information but used it differently. Stockholm cyclists focused on location, validating intentions with driving behaviour. Glasgow riders valued both cues equally. Muscat cyclists trusted interfaces, prioritising intentions without relying on driving behaviour. These insights are key for global AV adoption.

Virtual Reality (VR) locomotion methods are mainly ground-based, room-scale, or discrete, making them ill-suited for flying experiences. Although leaning- and controller-based techniques are promising for flying in VR, we lack empirical evidence of their advantages. We compared combinations of leaning- and controller-based methods for steering and velocity in a user study (N = 24) using a broom metaphor to integrate these methods into an understandable locomotion reference. The steering methods were: 1) controller-pointing (CP) and 2) headset-leaning (HL); and for velocity control: 1) controller linear displacement (CLD) and 2) headset linear displacement (HLD). Results indicate that HL increase presence compared to CP. However, combining HL with CLD worsens coin collection rate, completion time, mental load, control factor ratings, and enjoyment. In contrast, HLD worked well when paired with either steering method. CP-CLD led to the highest coin collection rate and lowest mental load. All methods had comparable feelings of flying.

Animations produced by generative models are often evaluated using objective quantitative metrics that do not fully capture perceptual effects in immersive virtual environments. To address this gap, we present a preliminary perceptual evaluation of generative models for animation synthesis, conducted via a VR-based user study (N = 48). Our investigation specifically focuses on animation congruency—ensuring that generated facial expressions and body gestures are both congruent with and synchronized to driving speech. We evaluated two state-of-the-art methods: a speech-driven full-body animation model and a video-driven full-body reconstruction model, assessing their capability to produce congruent facial expressions and body gestures. Our results demonstrate a strong user preference for combined facial and body animations, highlighting that congruent multimodal animations significantly enhance perceived realism compared to animations featuring only a single modality. By incorporating VR-based perceptual feedback into training pipelines, our approach provides a foundation for developing more engaging and responsive virtual characters.

Introduction: Social interactions incorporate various nonverbal signals to convey emotions alongside speech, including facial expressions and body gestures. Generative models have demonstrated promising results in creating full-body nonverbal animations synchronized with speech; however, evaluations using statistical metrics in 2D settings fail to fully capture user-perceived emotions, limiting our understanding of the effectiveness of these models. Methods: To address this, we evaluate emotional 3D animation generative models within an immersive Virtual Reality (VR) environment, emphasizing user—centric metrics-emotional arousal realism, naturalness, enjoyment, diversity, and interaction quality—in a real-time human-agent interaction scenario. Through a user study (N = 48), we systematically examine perceived emotional quality for three state-of-the-art speech-driven 3D animation methods across two specific emotions: happiness (high arousal) and neutral (mid arousal). Additionally, we compare these generative models against real human expressions obtained via a reconstruction-based method to assess both their strengths and limitations and how closely they replicate real human facial and body expressions. Results: Our results demonstrate that methods explicitly modeling emotions lead to higher recognition accuracy compared to those focusing solely on speech-driven synchrony. Users rated the realism and naturalness of happy animations significantly higher than those of neutral animations, highlighting the limitations of current generative models in handling subtle emotional states. Discussion: Generative models underperformed compared to reconstruction-based methods in facial expression quality, and all methods received relatively low ratings for animation enjoyment and interaction quality, emphasizing the importance of incorporating user-centric evaluations into generative model development. Finally, participants positively recognized animation diversity across all generative models.

Generative Artificial Intelligence (GenAI) chatbots start changing experiences with art for museum visitors by making them more interactive and engaging. However, it remains underexplored how GenAI chatbots influence visitors' in-field experience and interaction at art museums regarding finding information, engagement, and enjoyment compared to existing museum tour-guide applications. In this paper, we contribute the design and implementation of a smartphone-based chatbot that detects artwork, generates textual and auditory information, and interactively answers visitors' questions. To explore visitors' experience with it, we conducted a field experiment (N=30) at the National Art Museum, comparing it to the existing museum application. Our results indicate that visitors showed higher artwork engagement with the chatbot than the museum application. Moreover, they enjoyed an interactive experience using the chatbot to learn about the art collection and have equally preferred textual and auditory information representation.

Human footsteps play a significant role in everyday life, allowing individuals to discern the emotions, gender, and intentions of others solely from the sound of their footsteps. However, the influence of footstep sounds made when walking on different floor types in virtual reality (VR) environments when joining conversational groups remains unclear. In this paper, we present a controlled study (N=50) to assess the impact of five different floor types, associated with specific footstep sounds and visuals, on the persuasiveness of Embodied Conversational Agents (ECAs) when inviting participants to join a free-standing conversational group. We analyze routes taken by participants and the positions at which they join the group, which may be compliant or not with the agent’s request when approaching the group while walking on different virtual floor types. Our findings reveal that the type of floor being walked upon, defined by footstep sounds and visual appearance, significantly impacts the persuasiveness of ECAs and the trajectories taken by participants to join the group. Participants took longer paths and joined the group in the presence of more pleasant footstep sounds. Further, they tended to adhere to social norms by avoiding walking through the group’s center.

Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through brain activity, either passively or actively. In the past decade, BRI systems have advanced significantly, primarily leveraging electroencephalogram (EEG) signals. This article presents an up-to-date review of 87 curated studies published between 2018 and 2023, identifying the research landscape of EEG-based BRI systems. The review consolidates methodologies, interaction modes, application contexts, system evaluation, existing challenges, and future directions in this domain. Based on our analysis, we propose a BRI system model comprising three entities: Brain, Robot, and Interaction, depicting their internal relationships. We especially examine interaction modes between human brains and robots, an aspect not yet fully explored. Within this model, we scrutinize and classify current research, extract insights, highlight challenges, and offer recommendations for future studies. Our findings provide a structured design space for human-robot interaction (HRI), informing the development of more efficient BRI frameworks.

Social robots are employed as companions, helping in industrial and domestic environments. Adapting robots' capabilities to user needs can be achieved through teaching from human demonstrations. However, the influence of robots' preexisting proficiency and learning rate on human teachers' self-efficacy and perception of the robots is underexplored. In this paper, we simulated four robot performance types that combine: (1) preexisting proficiency (low/high) and (2) learning rate (slow/fast). We conducted a controlled lab experiment studying the impact of robots' performance type on teachers' self-efficacy, willingness to teach the robot, and perception of the robot (N=24), in which robots placed objects in suitable locations. Fast learners were perceived as more intelligent, anthropomorphic, and likable, and this caused higher teaching self-efficacy regardless of preexisting skills. Slow learners caused frustration while teaching. Moreover, participants stopped teaching robots with low preexisting skills sooner, regardless of the learning rate, indicating potential bias caused by expectations.

Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants. Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them. Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations. Prosocial behavior, such as yielding one's right of way, will be needed to resolve unclear traffic situations or foster traffic flow. However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles but for all road users? Here, we describe a new scale to measure perceived social behavior in urban traffic scenarios. Through an online survey on N = 318 individuals and a validation study, we developed the Situational Prosocial and Aggressive Behavior in Traffic Scale and assessed it psychometrically.

Oceans, lakes and rivers, dynamic and vital ecosystems, face increasing threats from climate change. To ensure its sustainability, there is an urgent need for technologies that promote responsible and sustainable human-water interactions. Water sports engagement fosters mental and physical health benefits, as well as environmental care when responsible practices are encouraged. Although prior work has investigated how interactive technology can support sports practice to make it sustainable, water sports are less explored due to the unique technical challenges they pose. Hence, there is an opportunity for human-computer interaction (HCI) to explore how interactive technology can be adapted to the dynamic, unpredictable nature of outdoor water sports to foster water conservation and ocean sustainability. We argue that by exploring the design of interactive water sports experiences through a soma design lens, we will better understand the intricate synergy between our bodies and the felt and lived body of water, hence, supporting meaningful body-water interactions. We aim to engage researchers in exploring the potential of soma design in the context of water and water sports guided by preliminary posthumanist water frameworks. The workshop outcomes include a design framework supporting engagement in outdoor water sports to foster sustainability through soma design. Insights from the workshop will be documented in a future academic publication to advance the WaterHCI field.