Mental health conversational agents (CAs) are gaining increasing attention as accessible tools for social communication, emotional support, and stress relief. These agents introduce new forms of human-AI interaction, yet the factors influencing user trust remain underexplored. Prior research suggests that conversation type and presumed message source may shape users’ experience, but their effects on users’ intentional stance and trust in CAs are not well understood. To address this gap, we first conducted a pre-study to develop a questionnaire for measuring users’ intentional stance towards mental health CAs. We then carried out a 2 × 2 mixed-design experiment to examine how conversation type and presumed message source influence intentional stance and trust, and whether intentional stance mediates the relationship between conversation type and trust. Results show that conversation type significantly influences user trust, mediated by intentional stance, while presumed message source had no significant effect. These findings advance our understanding of how users form trust in mental health CAs and offer implications for designing more engaging and trustworthy conversational systems in mental health contexts.

Human-robot touch interaction plays an essential role in emotional support and human mental health support. The role of therapeutic robots such as Paro in mental support has been widely investigated. However, the impact of humanoid robotic touch on the social regulation of users’ emotions is still unknown. Therefore, a mixed experiment was conducted with the type of touch (grip versus contact) as the between-subjects factor and the presence of touch during movie reception (with versus without touch) as the within-subjects factor. The subjective perception of emotion, ECG, and fNIRS signals were collected during the experiment. The results showed that robot touch regulates subjectively positive emotions, reduces HR, increases HRV, and helps suppress the brain activity on the right DLPFC. No main effect of touch type was found on the regulation effect of subjective emotions, autonomic responses, and central nervous responses. The study provides subjective and neurophysiological evidence for the great potential of humanoid robotics for the social regulation of emotion.

Anthropomorphic visual and auditory cues are two crucial design elements influencing users’ affective preference for chatbots. However, most earlier studies only focused on one of them and it is still unknown how anthropomorphic visual appearance and voice influence users’ affective preference and neural responses. In the current research, participants’ subjective preference evaluation and objective ERP responses were measured when being presented with chatbots with different visual and auditory cues. (human-like voice and mechanical voice). Subjective results indicated that consistent cues of anthropomorphic visual appearances and voices jointly evoked users’ higher affective preference for chatbots. Notably, auditory cues play a dominant role among audiovisual cues that influence users’ affective preference for chatbots. ERP results showed that low anthropomorphic visual appearances and mechanical voices jointly elicited larger P2 and P3. Additionally, chatbots with low anthropomorphic visual appearances and chatbots with human-like voices elicited larger LPP. These findings hold theoretic implications for understanding the impact of chatbots’ visual appearance and voice on users’ affective preference and provide practical insights for the design of human-chatbot interactions.

One prolific growth area for artificial intelligence (AI) is counselors for mental health. Earlier studies have reported that anthropomorphic features and haptic interaction can promote user engagement in conversations and foster the development of relationships between users and intelligent agents. This study examined the main and interaction effects of anthropomorphism and touch behavior on self-disclosure intention, attachment, and cerebral activity in the context of agents as AI mental health counselors (AIMHC). The results indicated that users tend to disclose information to the non-anthropomorphism AIMHC, regardless of with or without touch behavior. Users reported the highest attachment towards the anthropomorphism AIMHC with touch behavior. Additionally, privacy concerns and perceived empathy were determined as significant mediators. Moreover, anthropomorphism induced increased activity in the frontopolar area, correlating with self-disclosure intention. Anthropomorphism AIMHC’s touch behavior evoked the greatest increases in left DLPFC activity. This study explains the mechanism of effect and analyzes the theoretical and practical implications of these findings.

Measurement of users’ perception and visual behaviors to anthropomorphic design cues of chatbots can improve our understanding of chatbots and potentially optimize chatbot design. However, as two typical and basic features, how chatbot appearances and conversational styles jointly affect users’ perception and visual behaviors remains unclear. Therefore, this study conducted an eye-tracking experiment to explore users’ perception and visual behaviors. Results indicate that anthropomorphic appearances and human-like conversational styles jointly increased users’ perception of chatbots’ social presence, trust in chatbots, and satisfaction with chatbots. In contrast, on users’ visual behaviors, such a joint effect was not found, although chatbots with higher anthropomorphic appearances and human-like conversational styles triggered more fixation counts and longer dwell time. These findings suggest that anthropomorphic appearance and human-like conversational style can improve users’ perception and attract more visual attention to chatbots. These findings provide theoretical contributions and practical implications for relevant researchers and designers.

Identifying users’ diversified needs and designing products that match those needs is important in the era of mass personalization. Smart TV navigation interfaces lack good affective design, and difficulties remain in responding to diversified affective needs. Therefore, an affective design approach that considers the diversity of user needs is presented to optimize the design of smart TV navigation interfaces. First, the laddering interview is conducted to capture the diversified affective needs and multi-layer interface design elements perceived by users. Kansei distance is introduced to characterize diversified needs. Then, single-user relationship models between user perceptions and interface attributes are constructed. Finally, the two-stage interface optimization is performed to obtain common and individual optimization attributes, which are further embodied as parameter-layer optimization solutions through user experiments. This method is capable of capturing the diversified affective needs for smart TV navigation interfaces and achieving differentiated product design.

The rapid development of robot technology has introduced a substantial impact on manufacturing. Numerous studies have been carried out to apply collaborative robots (cobots) to address manufacturing productivity and ergonomics issues, which has brought extensive opportunities. In this context, a systematic literature search in the Web of Science, Scopus, and Google Scholar databases was carried out by electronic and manual search. Thus, 59 relevant contributions out of 4488 studies were analyzed by using preferred reporting items for systematic reviews and meta-analysis (PRISMA). To provide an overview of the different results, studies are summarized according to the following criteria: country, author, year, study design, robot category, results, and future opportunities. The effects of cobots on safety, system design, workplace design, task scheduling, productivity, and ergonomics are discussed to provide a better understanding of the application of cobots in manufacturing. To incentive future research, this paper reviews the development of cobots in manufacturing and discusses future opportunities and directions from cobots and manufacturing system perspectives. This paper provides novel and valuable insights into cobots application and illustrates potential developments of future human-cobot interaction.

The present study aimed to investigate how facial features, including facial ratio, eye shape, and mouth presence, influence users’ perceptions of social robots in terms of anthropomorphism, trustworthiness, and overall impressions. Using electroencephalogram (EEG) and eye-tracking technologies, we conducted a 2 (face ratio) × 2 (eye shape) × 2 (with or without mouth) full factorial experiment designed within-subject. EEG signals and pupil diameters were recorded while participants viewed images of robots with different facial design features. The results showed that the shape of robot’s eyes significantly influenced users’ perceptions, with round eyes being associated with higher ratings of anthropomorphism, trustworthiness, and overall positivity. Robots with lower facial width-to-height ratios (fWHR) induced smaller average pupil diameters in users than those with high fWHR. Additionally, robots with lower fWHR were perceived as more anthropomorphic, trustworthy and users produced higher theta rhythm when the eye shape was round but did not when it was rectangular. The presence of a mouth led to higher ratings of anthropomorphism rather than trustworthiness and other measures. Overall, the findings highlight the importance of facial features in shaping users’ perceptions of social robots and provide practical implications for designing social robots with anthropomorphic facial features.

Following the development of mobile internet and smart devices, designing smart product interfaces is positively significant to enhance user experience and improving the quality of life. Users automatically produce implicit evaluations when they encounter or use the interfaces of smart devices. However, what happens in our brain after receiving the visual information? How do we process visual information? How do the interface design features affect the subconscious evaluation process? To answer these questions, the current study aims to investigate the neural mechanism of users’ subconscious evaluation process of mobile interfaces in smart apps by using event-related potentials (ERPs) technology. ERPs results showed that P1, N1, and N2 can reflect participants’ subconscious evaluation of mobile interfaces in smart apps. In conclusion, participants could evaluate the more obvious layout feature but ignore the less obvious color feature of mobile news app interfaces automatically. Moreover, both the right-hemisphere and valence hypotheses were observed in the current study. These findings enrich the design of mobile interfaces and user cognition theory. Besides, the present study established a feasible technological method for the acquisition of users’ implicit needs and provides a data foundation for smart service. In addition, it can help app designers to identify users’ implicit needs and compare the alternative prototype in the development stage of mobile interfaces.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Short-form videos are popular worldwide as a thriving form of entertainment. Its fragmentation pattern, which presents users with intensive and engaging information, might lead to addiction and adverse effects. This study aims to investigate the effect of addiction to short-form videos on users’ attention, including attention while watching videos and the ability of attentional concentration after watching time. Users addicted or non-addicted to short-form videos were screened to participate in a short-form video watching task and a Stroop task based on eye-tracking technology. The results showed that addicted users reported less interest, centration, and more distractions and exhibited more fixation counts and shorter average fixation duration during watching short-form videos than non-addicted users. In the Stroop task, addicted users achieved longer response time and less accuracy and showed longer average fixation duration, more fixation counts, and saccades between the targets and the distractors than non-addicted users. The results suggest that addicted users might suffer more difficulties maintaining attention, have more attention deficits while watching short-form videos, and have impaired attentional concentration for processing interference. The findings contribute to understanding the effect of addiction to short-form videos and provide helpful insight into using it healthily and preventing addiction.

Appearance and voice are essential factors impacting users’ affective preferences for humanoid robots. However, little is known about how the appearance and voice of humanoid robots jointly influence users’ affective preferences and visual attention. We conducted a mixed-design eye-tracking experiment to examine the multisensory integration effect of humanoid robot appearances and voices on users’ affective preferences and visual attention. The results showed that the combinations of affectively preferred voices and appearances attracted more affective preferences and shorter average fixation durations. The combinations of non-preferred voices and preferred appearances captured less affective preferences and longer fixation durations. The results suggest that congruent combinations of affectively preferred voices and appearances might motivate a facilitation effect on users’ affective preference and the depth of visual attention through audiovisual complements. Incongruent combinations of non-preferred voices and preferred appearances might stimulate an attenuation effect and result in less affective preferences and a deeper retrieval of visual information. Besides, the head attracted the most amount of visual attention regardless of voice conditions. This paper contributes to deepening the understanding of the multisensory integration effect on users’ affective preferences and visual attention and providing practical implications for designing humanoid robots satisfying users’ affective preferences.

Users’ affective preference for voices has become a topic of great interest with the prevalence of humanoid robots. Nevertheless, the affective preference formation for humanoid voices remains unknown, and its evaluation lacks objective methods. Consequently, we conducted an EEG experiment to unravel the underlying neural dynamics and evaluate users’ affective preference for humanoid robot voices. Significantly larger P2, P3, and LPP amplitudes, enhanced theta, and decreased alpha oscillations were observed when users affectively preferred humanoid robot voices. The results suggest that the neural dynamics underlying users’ affective preference for humanoid robot voices might primarily consist of early detection of affective information in voices, further processing of affective information, and later evaluative categorization of affective preference. Moreover, the neural indicators could distinguish users’ affective preferences for humanoid robot voices. The study contributes to understanding the auditory affective preference formation for humanoid robot voices and providing a neurological evaluation method.

Even though humanoid robots are being applied to diverse areas, the formation of users’ preference for the appearance of humanoid robots remains unknown. The present study investigated users’ neural dynamics underlying preference formation to evaluate users’ preference for the appearance of humanoid robots. EEG signals were recorded in a preference categorisation task, and neural dynamics were analysed via event-related potentials and time–frequency analysis. The results showed that in the early stage, the preferred humanoid robot appearances elicited enhanced parieto-occipital N1, frontal P2, and early central and parieto-occipital theta rhythm power than the non-preferred appearances. In the later stage, the preferred humanoid robot appearances elicited enhanced scalp-distributed LPP and later central and parieto-occipital theta power than the non-preferred appearances. The results suggested that the formation of users’ preference for the appearance of humanoid robots has a distinctive dual-stage of neural dynamics. The study provides designers with an objective method in evaluating users’ preference for the appearance of humanoid robots.

Social media is becoming an important instrument for interpersonal communication. However, more and more users attempt to use social media passively and have negative emotional responses. The critical question arising is what causes people to use social media passively. Furthermore, the roles of social media fatigue and privacy concerns between the perceived overload and passive usage intentions have not yet been investigated in depth. The study aims to explore how perceived overload affects the passive usage intentions of social media users. The conceptual model incorporated “perceived overload,” “social media fatigue,” “privacy concerns,” and “passive usage intentions” into a cognition-affect-conation framework to reflect the influencing process. This study collected data from 335 users on mainstream social media platforms and analyzed it using the partial least square-structural equation model (PLS-SEM). The results show that perceived overload positively affects the passive usage intentions of mobile social media users. Particularly, it is found that privacy concerns and social media fatigue mediate the relationship between perceive overload and passive usage intentions. The mediating effect of social media fatigue is significantly stronger than privacy concerns. This study enriches the research of information system use. It also provides theoretical and practical implications for social media scholars and practitioners.

Salient object detection (SOD) aims to detect the most prominent objects and regions in the human vision. Since the RGB and depth modalities contain discrepant characteristics and convey the clues of different domains, how to explore the fusion of multi-modal information and the interaction of cross-stage features remain the key problems in RGB-D SOD. In this letter, we propose a cross-stage multi-scale interaction network (CMINet), consisting of a multi-scale spatial pooling (MSP) module and a cross-stage pyramid interaction (CPI) module to interweave the feature maps of different stages in a bottom-up and top-down way. In addition, we also design an adaptive weight fusion (AWF) module to weigh the importance of multimodality features and fuse them. Extensive experiments are conducted on 4 widely used datasets to validate the effectiveness of the proposed CMINet. The results demonstrate that our approach achieves state-of-the-art performance against other 11 methods under 4 evaluation metrics.

The increasing growth in the use of mobile news apps has raised questions on how their interface design features affect users’ gaze behaviours and behavioural performance. To tackle these issues, two experiments (visual browse and search tasks) were designed to investigate the impact of interface design features (colour and layout) on users’ gaze behaviours (fixation count, fixation time ratio and first fixation duration) and behavioural performance (task completion time and search accuracy) with a portable eye tracker. Twenty-four participants were recruited to browse and search for news in different mobile news interfaces with Chinese language. The results showed that mobile news apps with white interfaces attracted more attention and participants needed more time to comprehend the information of white interfaces in the visual browse task. Furthermore, participants achieved higher search efficiency in the visual search task by using interfaces with red keyword and LT-RP (Left Text-Right Picture). In addition, it is noticeable that participants paid more attention to the text than the pictures of mobile news interfaces, and most participants first looked at text and later observed pictures. The findings provide valuable and interesting insights for better understanding users’ gaze behaviours and behavioural performance of mobile news apps.

Affective computing focuses on technologies and theories that advance understanding of human affect, considering emotion and cognition in the design of related technologies to fulfill human needs, which gains substantial attention of researchers all over the world. To provide an insight into affective computing researches, this paper utilizes the method of bibliometric analysis to obtain information with respect to when and where the researches were performed by whom and how the mainstream contents evolved over the years. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis, including the analysis of the performance of countries, journals, institutes, authors and research hotspots. A total number of 1,625 documents published from 1999 ~ 2018 were screened to conduct quantitative analysis, which were retrieved in the Web of Science database with defined search terms. This paper can enable researchers to gain wider and deeper insight into affective computing researches in the last decades through bibliometric analysis, thereby facilitating relevant researchers having a general understanding of aggregate performance in the affective computing field and finding research directions in the future.

In an era of mobile Internet, the using of mobile interfaces is ineluctable in our daily life. To some extent, the first impression of the mobile user interfaces determines users’ downloading and using behavior, even affects the overall user experience. In fact, the underlying neural mechanism of users’ first impression formation of mobile user interface is worthy to be investigated. Considering the process of first impression formation is always unconscious and transient, event-related potentials (ERPs), which are used to track users’ cerebral activities could be an appropriate method to explore this process. In the present study, the perceived usability and esthetics are taken as the evaluation dimensions of user’ first impression formation, and ERPs were used to investigate which dimensions were automatically activated when users browsed the mobile user interfaces passively without any explicit guidance. The ERPs results showed that N2 was dominated by perceived usability and esthetics, which reflected in enhanced N2 for high esthetics than for low esthetics, and larger N2 for high-perceived usability/high esthetics compared to low-perceived usability/low esthetics. Moreover, there was a larger late positive potential (LPP) for low esthetics interfaces with respect to high esthetics ones. The findings revealed that users could make an implicit evaluation spontaneously to the mobile user interfaces with different levels of perceived usability and esthetics. Furthermore, users’ first impression is significantly affected by the differences in esthetics but marginally influenced by the difference in perceived usability. The suggestions from this study for mobile application designers might be that the esthetics design of the mobile user interface should be prioritized in the early design stage.

The design of humanoid robots’ emotional behaviors has attracted many scholars’ attention. However, users’ emotional responses to humanoid robots’ emotional behaviors which differ from robots’ traditional behaviors remain well understood. This study aims to investigate the effect of a humanoid robot’s emotional behaviors on users’ emotional responses using subjective reporting, pupillometry, and electroencephalography. Five categories of the humanoid robot’s emotional behaviors expressing joy, fear, neutral, sadness, or anger were designed, selected, and presented to users. Results show that users have a significant positive emotional response to the humanoid robot’s joy behavior and a significant negative emotional response to the humanoid robot’s sadness behavior, indicated by the metrics of reported valence and arousal, pupil diameter, frontal middle relative theta power, and frontal alpha asymmetry score. The results suggest that humanoid robot’s emotional behaviors can evocate users’ significant emotional response. The evocation might relate to the recognition of these emotional behaviors. In addition, the study provides a multimodal physiological method of evaluating users’ emotional responses to the humanoid robot’s emotional behaviors.