International Journal of Human-Computer Studies最新文献

As visualisations reach a broad range of audiences, designing visualisations that attract and engage becomes more critical. Prior work suggests that semantic icons entice and immerse the reader; however, little is known about their impact with informational tasks and when the viewer’s attention is divided because of a distracting element. To address this gap, we first explored a variety of semantic icons with various visualisation attributes. The findings of this exploration shaped the design of our primary comparative online user studies, where participants saw a target visualisation with a distracting visualisation on a web page and were asked to extract insights. Their engagement was measured through three dependent variables: (1) visual attention, (2) effort to write insights, and (3) self-reported engagement. In Study 1, we discovered that visualisations with semantic icons were consistently perceived to be more engaging than the plain version. However, we found no differences in visual attention and effort between the two versions. Thus, we ran Study 2 using visualisations with more salient semantic icons to achieve maximum contrast. The results were consistent with our first Study. Furthermore, we found that semantic icons elevated engagement with visualisations depicting less interesting and engaging topics from the participant’s perspective. We extended prior work by demonstrating the semantic value after performing an informational task (extracting insights) and reflecting on the visualisation, besides its value to the first impression. Our findings may be helpful to visualisation designers and storytellers keen on designing engaging visualisations with limited resources. We also contribute reflections on engagement measurements with visualisations and provide future directions.

Augmenting visual art in art galleries can be an effective Audio Augmented Reality (AAR) application for indoor exploration. In the current study, eight paintings from four genres were augmented with audio through their sonification. Basic Audio was generated using a sonification algorithm by identifying the major colors of the paintings, and Enhanced Audio was generated by a musician enhancing the Basic Audio; these were presented with the paintings to compare against No Audio. Twenty-six participants viewed each painting in all three conditions; eye gaze metrics, and qualitative data were collected. Results showed that Enhanced Audio led to significantly greater engagement and positive sentiments, compared to Basic Audio. Thematic analysis showed semantic and syntactic relationships of the audio with the paintings, and a tendency to guide users’ gaze over time. Findings from this study can guide future AAR developments to improve auditory display designs to enhance visual experiences.

This study investigates the effects of key sizes, typing angles, and typing techniques on typing productivity, biomechanics (muscle activity), and subjective experience with a mixed reality keyboard. The findings suggest that using smaller key sizes, such as 16 mm, may not be suitable due to slower typing speed, lower accuracy, lower user experience, higher muscle activity, and higher motion sickness. Typing with both index fingers results in the highest typing speed while using only a single index finger provides higher accuracy. Placing the keyboard at eye height leads to the highest typing speed, as participants can easily view the keys and the virtual environment simultaneously. However, typing accuracy is not affected by typing angle and typing technique. Implementing these findings on the virtual keyboard design could potentially benefit workers’ productivity and decrease errors in the mixed reality environment.

Target selection is a crucial task in augmented reality (AR). Recent evidence suggests that user motion can significantly influence target selection. However, no systematic research has been conducted on target selection within varied intensity user motions and AR settings. This paper was carried out to investigate the effects of four user motions (i.e., standing, walking, running, and jumping) and two viewing modes (i.e., viewpoint-dependent and viewpoint-independent) on user performance of target selection in AR. Two typical selection techniques (i.e., virtual hand and ray-casting) were utilized for short-range and long-range selection tasks, respectively. Our results indicate that the target selection performance decreased as the intensity of user motion increased, and users demonstrated better performance in the viewpoint-independent mode than in the viewpoint-dependent mode. We also observed that users took a longer amount of time to select targets when using the ray-casting technique than the virtual hand technique. We conclude with a set of design guidelines to improve the AR target selection performance of users while in motion.

Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent’s effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent’s recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

Recent developments in accessible electronic-making toolkits have opened up avenues for individuals with intellectual disabilities(ID) to actively participate in creating their own smart objects based on the Internet of Things (IoT) technology. These toolkits present a novel opportunity to foster the inclusion of this often-under-considered community in the development of personalized solutions that can impact their autonomy and well-being. However, understanding IoT encompasses comprehending the inter-object connection paradigm at the core of this technology, an aspect that is not adequately covered by the existing accessible toolkits. To fill this gap, this paper illustrates the design and evaluation of MakeNodes, a tangible toolkit to involve people with ID in making smart-thing networks in a guided, collaborative, and engaging way. The toolkit comprises a series of input and output nodes that can be paired to make any object or surface smart. The paper illustrates how MakeNodes empowers people with ID through IoT-making activities that promote collaborative work to address personal needs. Key findings are discussed in the form of lessons learned concerning the importance of physical and visual elements, hands-on exploration, and interaction robustness in improving the accessibility of IoT-making toolkits for people with ID.

Rising unemployment has led to many discouraged job seekers. While the impact of job seekers’ motivation on interview performance is acknowledged in previous research, little attention has been given to understanding the effect of training on interview motivation and performance. We present InterviewApp, an online interview training tool aiming to support interview motivation through autonomy, relatedness and competence needs derived from Self-Determination Theory and, in turn, performance. Through a four-month study (N=135), we assess its effectiveness in supporting job seekers’ interview motivation and performance. Our results demonstrate the role of autonomy in mediating the effect of training on performance. We found that the intervention significantly affected the job seekers’ perceived autonomy. Furthermore, engagement with the recording and feedback features of the tool positively impacted performance. Overall, job seekers found InterviewApp helpful for online interview training and valued the provided expert feedback. These findings have implications for the design of online interview training tools and for behaviour change interventions to support employment.

Users of Social Networking Sites (SNS) often engage in social comparison, an experience that can elicit various affective responses. While previous studies have established important links between social comparison and emotion, the granularity and ecological validity of this work remains limited. To address this issue, we conducted a field study with 40 Instagram users. We unobtrusively recorded their Instagram usage patterns (e.g., time on site and the type of content consumed, such as feed, story, or message). Additionally, we used experience sampling methods and questionnaires to capture their affective experiences, social comparisons, and depression severity. Our results elucidate the interplay between these emotions, traits, and tendencies during Instagram use. Notably, we find that while passive or prolonged Instagram use affects users’ emotions, the magnitude of this effect depends on the frequency of users’ social comparisons. These results can inform the design of systems to improve mental well-being on SNS.

As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach – majority voting – to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR – by approximately 9% and 31%, respectively – compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.

When we converse, we adapt our behaviors to our interlocutors. The adaptation can serve to indicate our engagement which can also elicit enhancement of the involvement of others. Virtual agents (or socially interactive virtual agents) that play the role of interaction partners can improve the human users’ interaction experience by displaying continuous and adaptive behaviors in real time. Virtual agents have been used in multiple domains to improve user interaction and performance. The promising results of the endowment of adaptation to agents in increasing the agents’ perception and user experience were shown in previous studies. In this paper, we develop an adaptive virtual agent that renders real-time adaptive behaviors based on the behaviors shown by its human interlocutor. The ASAP model rendering reciprocally adaptive agent behavior was employed to realize the system. The system consists of four main parts: perception of social signals, agent adaptive behavior generation, agent visualization (i.e. rendering of the agent’s verbal and nonverbal behavior), and communication of signals. To showcase the usefulness of our adaptive agent, as a proof-of-concept we choose the e-health application of cognitive behavior therapy (CBT), which identifies and rectifies biased and irrational thoughts (or automatic thoughts). Through this study, we show the importance of giving the agent reciprocal adaptation capability notably in enhancing the user experience and the effectiveness of the CBT session. We validate the importance of endowing such adaptation capability by studying the difference between agents that are reciprocally adaptive, solely expressive (with mismatched behavior), and inexpressive (in a still posture) via questionnaires and measures related to the agent perception (naturalness, human-likeliness, synchrony, and engagement) for user experience and the CBT effectiveness (mood, anxiety, stress, and cognitive change). These results highlight the value of making virtual agents adapt in real time. This could lead to agents being capable of providing more personalized and interactive experiences for a wide range of applications. Also, we have collected a new human-agent interaction (HAI) database, HAI-CBT database, which is publicly available to the research community.