Human–robot collaboration increasingly frames robots as teammates rather than tools, yet there is limited guidance on how robots should respond when failures are attributed to the human collaborator. We investigate how robot collaborators should respond to support collaboration experience after a human-attributed failure. In a 4 × 2 mixed factorial design (N = 60), participants completed a collaborative block-stacking task with either a humanoid robot (NAO) or a human collaborator under four scenarios: success, affective feedback, cognitive feedback, and no feedback. We measured collaboration experience in terms of teamwork quality, perceived copresence, and intimacy. Both affective and cognitive feedback improved these outcomes compared with no feedback: affective cues yielded the strongest socio-relational gains (copresence, intimacy), whereas cognitive cues more strongly enhanced perceived teamwork quality. These patterns were consistent across human–robot and human–human collaboration, indicating shared team-level expectations that extend beyond the individual actor. The results provide empirical evidence for socially adaptive robots that pair brief emotional reassurance with concrete guidance to support collaboration after human-attributed failures.

Large language models (LLMs) have evolved into LLM agents that can use the user conversation context and respond according to the roles of the LLM agents. Recent studies have suggested that LLM-based agents can be used as human-like partners in social interactions. However, the role of the environmental context, particularly spatial information of user space, in the interaction between humans and LLM agents has not been explored. In this study, participants engaged in counselling conversations under three different conditions based on their spatial awareness levels. The dependent measures included copresence, trust, therapist alliances, and self-disclosure. The results suggested that participants in the condition where the LLM actively reflected spatial information generally reported higher levels of user experience. Interestingly, when the LLM actively reflected the spatial context of the user, the participants tended to describe themselves and express their emotions more. These findings suggest that spatially aware LLM agents can contribute to better social interactions between humans and LLM agents. Our findings can be used to design future augmented reality applications in the counselling, education, and healthcare industries.

Recent studies have demonstrated that brain synchrony can indicate the quality of social interaction in real-world communication. However, there is a lack of research on measurement of brain synchrony during social interactions in remote AR. In this study, we investigated the brain synchrony of remote augmented reality (AR; Study 1) and face-to-face (FTF; Study 2) interactions. Functional near-infrared spectroscopy was used to measure the brain synchrony during the tangram puzzle task. In a collaboration condition, participants worked together to solve the puzzle. In an individual condition, participants solved the puzzle independently. We recruited 46 participants in Study 1 and 48 participants in Study 2. Study 1 showed there was a significant difference in brain synchrony between the individual and collaboration conditions, and a positive correlation was observed between brain synchrony and the task performance in the collaboration condition. A comparison between Study 1 and 2 suggested that the difference between the collaboration and individual conditions was maintained, and some differences were observed in the brain synchrony between the AR and FTF interactions. These results suggest that measurement of brain synchrony is beneficial for social interaction in remote AR collaborations. The implications of these results on future remote interactions are discussed.

Previous education studies suggested that “warming up” activities featuring interpersonal synchrony components help improve educational quality and social closeness between teachers and students. While educators are eager to incorporate augmented reality (AR) into remote educational settings, prior research has not investigated the beneficial impacts of interpersonal synchrony on remote AR education. This study investigates the effects of prior physical synchrony (PS) on learning outcomes, inter brain synchrony (IBS), and social closeness in remote AR education. 41 participants were recruited for learning English vocabulary from a teacher. They conducted a movement learning task using prior PS and physical asynchrony (PA). The results showed that the positive effects of warming up activity on quiz score, IBS, and social closeness were observed in the PS section, but not in the PA section. Moreover, we found a high correlation between quiz score and IBS but not with social closeness. These results suggest that prior PS is a beneficial component of remote AR education and that IBS could be used as an index for evaluating educational quality. The implications of these results for remote AR education are discussed.

Emotion detection using features presented in the body has been comparatively understudied compared to other emotional modalities. This study investigated and compared how emotions are revealed through bodily sensations and body movement information. We propose a novel visualization method for addressing body part activation or deactivation associated with different emotions using motion capture data (bodily motion maps; BMMs) and aim to compare its emotional features with existing methods of addressing sensation towards body activation or deactivation using the computerised self-report method (bodily sensation maps; BSMs). A user study is conducted with twenty-nine participants to gather BSMs and BMMs under seven emotional statuses (happy, sad, surprise, angry, disgust, fearful, and neutral). The results indicate that people's BMMs as well as their BSMs contain shared features that can discriminate between emotions. The asynchronies between bodily sensation and body motion existed. Furthermore, convolutional neural network (CNN)-based automatic emotion recognition outperforms humans’ emotion recognition abilities. The implications of the current study's method of comparing emotional features between bodily sensation and body motion for future emotion-related studies for mental disorders are discussed.

As one of autonomous vehicles, conditional autonomous vehicles is expected to become popular in the near future. Conditional autonomous vehicles can send a take-over request (TOR) to a driver, and if they are immersed in non-driving-related tasks (NDRT), they will struggle to accommodate this request. Previous studies have shown that providing augmented reality (AR) information on traffic situations (status cues) or driver actions (command cues) can improve TOR performance. However, we are not aware of any studies comparing the types of AR cues (state versus command cues) and their interactions with environmental factors. Therefore, the current study investigated this and evaluated the TOR performance of 42 drivers. We used a 2 (environments: day and night) $\times$ 4 (AR cue types: without, status, command, and combined cues) mixed-subject experimental design, and dependent measures included driving, cognitive, and NDRT performances. The results suggest that overall driving and cognitive performance were significantly improved by the command AR cue. In contrast, the status AR cue improved the TOR performance in nighttime environments. The performance of AR cues can vary depending on environmental factors, and AR cue designs for autonomous vehicles should consider this interaction for successful collaboration between drivers and vehicles.

Recent advances in technology have allowed users to experience an illusory feeling of full body ownership of a virtual avatar. Such virtual embodiment has the power to elicit perceptual, behavioral or cognitive changes related to oneself, however, its emotional effects have not yet been rigorously examined. To address this issue, we investigated emotional changes as a function of the level of the illusion (Study 1) and whether changes in the facial expression of a virtual avatar can modulate the effects of the illusion (Study 2). The results revealed that stronger illusory feelings of full body ownership were induced in the synchronous condition, and participants reported higher valence in the synchronous condition in both Studies 1 and 2. The results from Study 2 suggested that the facial expression of a virtual avatar can modulate participants' emotions. We discuss the prospects of the development of therapeutic techniques using such illusions to help people with emotion-related symptoms such as depression and social anxiety.