International Journal of Human-Computer Studies最新文献

The global pandemic has pushed human society into a mental health crisis, prompting the development of various chatbots to supplement the limited mental health workforce. Several organizations have employed mental health survey chatbots for public mental status assessments. These survey chatbots typically ask closed-ended questions (Closed-EQs) to assess specific psychological issues like anxiety, depression, and loneliness, followed by open-ended questions (Open-EQs) for deeper insights. While Open-EQs are naturally presented conversationally in a survey chatbot, Closed-EQs can be delivered as embedded forms or within conversations, with the length of the questionnaire varying according to the psychological assessment. This study investigates how the interaction style of Closed-EQs and the questionnaire length affect user perceptions regarding survey credibility, enjoyment, and self-awareness, as well as their responses to Open-EQs in terms of quality and self-disclosure in a survey chatbot. We conducted a 2 (interaction style: form-based vs. conversation-based) $\times$ 3 (questionnaire length: short vs. middle vs. long) between-subjects study (N=213) with a loneliness survey chatbot. The results indicate that the form-based interaction significantly enhances the perceived credibility of the assessment, thereby improving response quality and self-disclosure in subsequent Open-EQs and fostering self-awareness. We discuss our findings for the interaction design of psychological assessment in a survey chatbot for mental health.

Unmanned Aerial Vehicle (UAV) control interfaces are critical channels for transferring information between the vehicle and an operator. Research on system performance has focused on enhancing vehicle automation and some work has evaluated cognitive workload for existing UAV interfaces. The potential for usable interface design to reduce cognitive workload during the early design phase has been largely overlooked. This study addresses these gaps by: (1) evaluating the effectiveness of a contemporary UAV interface design tool (the Modified GEDIS-UAV) to moderate user workload; (2) examining the effectiveness of various UAV interface designs for minimizing cognitive workload under different control task pacing; and (3) exploring the use of eye tracking measures, traditionally applied in other domains, as indicators of cognitive workload in UAV operations. We prototyped three different interface designs, classified as “baseline”, “enhanced” and “degraded” interfaces. Cognitive workload in UAV operation was manipulated in terms of levels of vehicle speed (“low” and “high”). Physiological and subjective measures of workload were collected for all combinations of interface design and task demand. Results revealed the “enhanced” interface to yield the lowest operator cognitive workload and supported operator resilience to increased control task demand, as compared to the “baseline” and “degraded” interfaces. In addition, task demand was found to elevate operator cognitive workload, particularly in terms of "mental" and "temporal" demands and operator perceptions of "performance". The study also demonstrated utility of eye-tracking technology for detecting cognitive workload in UAV operations. This research provides practical guidance for UAV control interface design to manage operator workload. The methods employed in the study are applicable to interface evaluation for various types of UAVs and other unmanned systems to enhance human-automation interaction.

Gesture elicitation study is an effective method to design gestures for various contexts. Through involving end-users, GES results in intuitive gestures because they directly reflect end-users’ mental models and preferences. However, limited by personal experience, end-users are not capable of taking full advantages of technology while proposing gestures, which is referred as legacy bias. To overcome this, previous studies demonstrate that users’ performance can be improved by priming, such as viewing gestures, watching fictional movies, and experiencing framed scenarios. This research extends this line of studies by considering the developmental origin of image schemas in priming. More specifically, we compared the influences of no-priming, priming with early image schemas (EIS), and priming with late image schemas (LIS) on GES. Controlled experiments were conducted (N = 120) along the three stages of GES: users’ generation of gestures (Experiment 1), final gesture sets (Experiment 2), and end-users’ learnability of gestures (Experiment 3). Results show that users are largely influenced by developmental origin of image schemas in priming. LIS-priming improve gesture proposal production in comparison to no-priming condition. As for end-users’ evaluation, EIS-priming gestures exhibit higher initial and overall learnability.

Players’ basic psychological needs for autonomy, competence, and relatedness are among the most commonly used constructs used in research on what makes video games so engaging, and how they might support or undermine user wellbeing. However, existing measures of basic psychological needs in games have important limitations—they either do not measure need frustration, or measure it in a way that may not be appropriate for the video games domain, they struggle to capture feelings of relatedness in both single- and multiplayer contexts, and they often lack validity evidence for certain contexts (e.g., playtesting vs experience with games as a whole). In this paper, we report on the design and validation of a new measure, the Basic Needs in Games Scale (BANGS), whose 6 subscales cover satisfaction and frustration of each basic psychological need in gaming contexts. The scale was validated and evaluated over five studies with a total of 1246 unique participants. Results supported the theorized structure of the scale and provided evidence for discriminant, convergent and criterion validity. Results also show that the scale performs well over different contexts (including evaluating experiences in a single game session or across various sessions) and over time, supporting measurement invariance. Further improvements to the scale are warranted, as results indicated lower reliability in the autonomy frustration subscale, and a surprising non-significant correlation between relatedness satisfaction and frustration. Despite these minor limitations, BANGS is a reliable and theoretically sound tool for researchers to measure basic needs satisfaction and frustration with a degree of domain validity not previously available.

Virtual reality biofeedback systems for relaxation training can be an effective tool for reducing stress and anxiety levels, but most of them offer a limited user experience associated to the execution of a single task and a biofeedback mechanism that reflects a single physiological measurement. Furthermore, user evaluations of such systems do not typically include a placebo condition, making it difficult to determine the actual contribution of biofeedback. This paper proposes a VR system for breathing and relaxation training that: (i) uses biofeedback mechanisms based on multiple physiological measurements, (ii) provides a richer user experience through a narrative that unfolds in phases where the user is the main character and controls different elements of the virtual environment through biofeedback. To evaluate the system and to assess the actual contribution of biofeedback, we compared two conditions involving 35 participants: a biofeedback condition that exploited real-time measurements of user's breathing, skin conductance, and heart rate; and a placebo control condition, in which changes in the virtual environment followed physiological values recorded from a session with another user. The results showed that the proposed virtual experience helped users relax in both conditions, but real biofeedback produced results that were superior to placebo biofeedback, in terms of both relaxation and sense of presence. These outcomes highlight the important role that biofeedback can play in virtual reality systems for relaxation training, as well as the need for researchers to consider placebo conditions in evaluating this kind of systems.

Object manipulation is the most common form of interaction in virtual reality. We introduced an efficient and ergonomic Big-Arm method to improve the efficiency and comfort of manipulating distant objects in virtual reality. We prolong the upper arm and forearm lengths according to the maximum distance of the manipulation space and construct the linear mapping between the real and virtual elbow angle, which makes manipulation easier to control and more efficient. We propose an optimized elbow angle mapping to further improve the efficiency and comfort of distant object manipulation. Two user studies were designed and conducted to evaluate the performance of our optimized Big-Arm method. The results show that our method achieves significant improvement in efficiency, ergonomic performance, and task load reduction for manipulating the distant object (distance $\ge$6 m) compared to the state-of-the-art methods. At the same time, our method exhibits superior usability.

Achieving effective collaboration and trust has been shown to be harder for virtual vs. in-person teams. Related work has confirmed that introductions are a key mechanism to form trust in newly-formed teams. The rise of remote work necessitated by the COVID-19 pandemic highlighted the need for accelerating the development of effective collaboration in virtual teams. In response to this need, based on the research around introductions and trust in team settings, we have developed a novel approach for virtual introductions. This is what we term as asynchronous, semi-guided, professional introductions. Participants pre-record an introduction that uses a set of professionally-focused questions and watch these introductions prior to the meeting. Our study examines the impact of these introductions on virtual teams in three conditions: video, text, and no introduction. In this study, we used the Team Diagnostic Survey post-task completion to assess team effectiveness and interpersonal processes of 28 dyads. Thematic coding was used to collect dyads’ experience and engagement. The introduction conditions demonstrated significantly improved collaboration, effectiveness, and engagement amongst participants. Notably, the video condition was particularly well-received by participants and resulted in higher levels of engagement and effectiveness compared to the text and no introduction conditions. Ultimately, the use of these introductions led to a marked increase in trust and collaboration amongst participants. We reflect on the effects of this finding in the mainstream and propose further research to support newly-formed virtual dyadic teams to increase team effectiveness. This study contributes to the existing literature by introducing a novel asynchronous, semi-guided approach to virtual dyadic team introductions, offering insights crucial for contemporary remote work dynamics.

Technologies support our everyday lives, and to ensure that people are not routinely excluded they must be usable by the wider population. However, technologies are not commonly tested with participants from a range of backgrounds. This paper reports on interviews and roundtable discussions with people whose identities can be underrepresented in usability testing and usability researchers to discuss how equality, diversity and inclusion (EDI) can be embedded in usability testing.

Key findings include (1) when people participate in research they need a sense of value, trust and agency, and (2) challenges for researchers for embedding EDI in usability testing include organisational pressures, stakeholder culture, getting guidance and recruiting who you need. Recommendations are made to researchers, and to the organisations that employ them. Additionally, we propose a research agenda for a community of users, creators of services and products, usability researchers, and all those advocating for EDI in usability research.

Misinformation is rampant on social media, and existing platform-supplied interventions offer limited effectiveness. In this study, we examine the effectiveness of credibility labels that dispute the accuracy of information when they are supplied by one’s peers at different levels of relationship closeness and political agreement. We investigate four variants of these labels using a 2 (strong vs. weak tie strength) x 2 (high vs. low political agreement) between-subjects factorial design. We find that credibility disputes raised by one’s co-partisans (peers with similar political beliefs) significantly reduced belief in misinformation, irrespective of one’s relationship closeness with the peer. Our findings also reveal that in contrast to prior literature, a peer’s knowledgeability may be more potent than trustworthiness in causing belief change, and that trust can sometimes manifest even in the credibility judgement of distant peers, when perceived to have expertise or a fact-checking tendency. We further highlight the dual nature of these credibility labels, discussing scenarios in which disputes by hyper-partisan members of the opposite party can enforce belief in misinformation. We conclude by discussing how peer-supplied credibility disputes can benefit social media, especially echo chambers with high political homophily, where disputes by a co-partisan may be met with less resistance and persuade significantly reduced belief in fake news.

The purpose of this study is to examine the impact of an example-based explainable artificial intelligence (XAI) interface on trust, understanding, and performance in highly-technical populations. XAI studies often focus on general users in low-risk domains. This study examined the impact of showing the closest matches from the training data from two classes on trust, understanding, and performance for highly-technical users in a high-risk domain. We found that providing example-based explanations significantly increased trust and understanding without decreasing performance. Showing the most similar examples from two classes increased trust more than showing examples from only one class. Participants did not treat different classes the same. The most important features for predicting how well an interface was understood were the helpfulness of the provided examples and the person's trust in the human-machine team. We found priming of highly-technical participants to be particularly important for running XAI studies to mitigate the fear of their jobs being impacted.