International Journal of Human-Computer Studies最新文献

Driver distraction by smartphone use is a major contributor in traffic accidents. Context-sensitive driver (in)attention monitoring and warning systems might mitigate the associated risks. However, few naturalistic studies are yet available on the effects of such systems. In this paper, utility of context-sensitivity in inattention monitoring was studied by a smartphone-based context-sensitive distraction warning system. The warnings were based on driver's phone use and the attentional demands of the upcoming traffic environment. The system's effects on 26 heavy smartphone users’ phone usage and acceptance were analyzed after a within-subject naturalistic study with 12 weeks of control (warnings off) and 12 weeks of interventions (warnings on). The system decreased odds that the drivers would touch their smartphones in reminder areas that were defined a priori as high demanding for attention. Against expectations, the system had no effect in urban road environments. The drivers reported that they had paid more attention to traffic because of the system and that the warnings were acceptable and useful, even if annoying. Similar systems’ safety effects should be further studied. No eye-tracking or driving performance measures were collected and thereby it is questionable if there was a true positive effect on participants’ attention. However, the findings suggest that (in)attention warning systems might benefit from adaptation of the warnings to the upcoming driving demands. The findings can be utilized for the development of proactive and context-sensitive (in)attention monitoring and distraction mitigation systems.

Although many methods currently exist to evaluate a user interface, they have been mainly developed and applied for graphical and vocal user interfaces, leaving aside other modalities such as gesture interaction. As a consequence, the evaluation of the global quality of a gesture user interface most often resorts to these methods, which take little or no explicit and specific account of the gesture modality or which adapt existing methods in a way that jeopardizes their validity. To remedy this situation, this paper introduces, defines, and justifies a method for evaluating explicitly and specifically a gesture user interface based on a conceptual model that consists of: (1) six quantitative measures covering user and system aspects, such as gesture thinking time and recognition rate; (2) three new qualitative scales, i.e., discoverability, learnability, and social acceptability, combined with seven scales in a gesture evaluation scheme, which is formally based on four measures, i.e., subscale score, subscale importance, scale mean score and scale mean importance; (3) a debriefing interview to cross-analyze the results of the quantitative measures and qualitative scales. This method also includes the use of multiple sessions to take into account short and long-term memory, as well as discoverability, and integrates tests specific to each gesture, as well as concrete use cases. For validation and illustration purposes, this method is applied to a gesture user interface serving as a case study. The results are then analyzed, starting with classic methods and gradually adding the particularities of our method to highlight its added value. Based on this experiment, we suggest some implications for evaluating gesture interfaces and for incorporating these scales into UEQ+, a modular user interface evaluation method.

Advances in haptic technologies can alter how real objects feel to our touch and create the experience of haptic augmented reality (AR). However, the definition, use cases, and value to end users of such haptic AR remain unclear. Existing work is concerned with technological implementation and lacks a user-centered perspective. To address these limitations, we analyze haptic AR systems in the literature to understand what constitutes haptic AR, why we would want to alter our sense of touch, and how haptic AR interactions take place. To demonstrate the value of studying haptic AR in the context of real-world tasks and user impressions of the concept itself, we also conducted a small exploratory study with five prototypical applications of different haptic AR systems. Our analysis highlights unexplored areas for haptics and HCI researchers and the need to conduct user evaluations of the overall concept rather than just point examples.

The avatar is a digital character that represents a person in a virtual environment, such as the metaverse. It is widely used in games and is also becoming a reality in other domains. Research indicates that the characteristics of a person’s avatar can influence his/her behavior while using a system, improving the sense of presence, immersion, and self-expression. A gap is how to support developers proposing avatar personalization, considering a user’s context, and understanding how context-awareness applied to avatars influences self-expression. In this article, we investigate how to support the development of self-expressive avatars. We present VISHnu, an approach developed to support the formalization of the use of context for avatar personalization. We conducted three evaluations to investigate the potential and feasibility of the VISHnu approach with specialists and developers: a focus group (n $=$ 6), interviews (n $=$ 6), and a questionnaire (n $=$ 12). In addition to guidelines derived from mixed qualitative methods and data triangulation, including insights from the literature review, we present two tangible artifacts—the checklist and glossary. These artifacts provide a robust initial foundation for developers and enthusiasts aiming to create avatar customization solutions aligned with advancements in immersive worlds like the metaverse. Our results have the potential to contribute to the creation of avatar personalization solutions that are more inclusive and more representative of human diversity.

While numerous studies have examined 3D interaction techniques for Virtual Reality (VR) across various tasks and scenarios, limited research has focused on music-related applications. However, the most common input techniques in consumer VR systems have been developed outside of the musical domain. Therefore they have not been tested in tasks where synchronization with auditory stimuli and timing plays a crucial role. There is a lack of empirical knowledge about performance and user experience. This paper presents a comparison of five selection input techniques for VR employing the tapping paradigm commonly utilized in the research on sensorimotor synchronization. We assess asynchrony and timing variance as well as user experience, encompassing factors such as ease of use, workload, and cybersickness of such techniques. The study involved 30 participants, both with and without musical expertise, and encompassed the examination of all techniques using one and two hands. Our analysis yielded several key findings: (1) different input techniques yielded distinct outcomes regarding timing asynchrony and variance; (2) the choice of interaction metaphor significantly influenced the user experience; (3) tracking stability emerged as a critical factor. Building upon these insights, we identified essential considerations for selecting the most suitable technique for music creation in VR and proposed design guidelines and future research directions in this domain.

The value of personal health data continues to be a debated topic in HCI and society more broadly. We investigate the monetary value people attach to their health data. Using a custom mobile app for 14 days with 55 participants, we collected health data (sleep duration, sleep quality, pain intensity, wake-up times) and a daily monetary data valuation using a reverse second-price auction. Participants bid to sell their data to a for-profit company, the government, or academia. Our findings indicate that people value their data differently based on who is buying. We also show that people are interested in monetizing their personal health data despite privacy and data protection concerns. The presented study helps us understand the data value landscape and paves way to a healthier data-driven future where people may benefit more from their own contributions, either in monetary or other forms.

Metacognitive monitoring, defined as the self-awareness and management of cognitive processes, influences creative design. Yet, there are few tools to enhance metacognitive monitoring through biofeedback. To address the gap, we present “Multi-Self”, a BCI-VR design tool for enhancing metacognitive monitoring in architectural design. Multi-Self evaluates designers’ emotions responses (valence and arousal) to their work, providing real-time, visual biofeedback. A proof-of-concept pilot study with 24 participants was conducted to assess the tool’s feasibility and usability. While feedback accuracy responses were mixed, most participants found the tool useful, reporting that it sparked metacognitive monitoring, encouraged exploration of the design space, and helped to modulate subjective uncertainty.

We discuss the state-of-the-art and future directions of the development, evaluation, and application of computational cognitive models for human-automated vehicle interaction. The capabilities of automated vehicles are rapidly increasing and changing human interaction with and around the vehicle. Yet, at the same time, fully automated vehicles that do not require human interaction are not available. Therefore, systems are needed in which the human and the vehicle interact together. We discuss how computational cognitive models that can describe, predict, and/or anticipate human behavior and thought can play a crucial role in this regard. Such research comes from many different disciplines including cognitive science, human-computer interaction, human factors, transportation research, and artificial intelligence. This special issue brings together state-of-the-art research from these fields. We identify four broader directions for future research: (1) to continue Allen Newell's research agenda for cognitive modeling, but now apply it to the field of human-automated vehicle interaction; (2) to move from isolated theory-slicing to integrated theories, (3) to consider cognitive models both for analysis of interaction and for use in embedded systems; (4) to move from models that mostly describe to models that can predict.

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

An interactive application’s overall pace of interaction is a combination of the user’s pace and the system’s pace, and if the system’s pace is mismatched to the user’s pace (e.g., timeouts or animations are too fast or slow for the user), usability and user experience can be impaired. Through a series of four studies, we investigated whether users prefer systems where the system’s pace better matches their own pace. All of the studies used common drag-and-drop interactions with hierarchical folder widgets, in which a folder would expand when the cursor hovered over it for a timeout period. If the system pace in these interactions is too fast (i.e., the timeout is too short), then the user’s performance and subjective experience is likely to be impaired because of unintended expansions; and if the system pace is too slow (i.e., the timeout is too long), then performance and experience could be impaired by unnecessary delay before folders expand. The first experiment was designed to validate the premise that fast-paced users prefer a fast system pace to a slow one (and the inverse for slow-paced users), and results confirmed this premise. The second study used the first experiment’s data to look for measures of user pace that could enable automatic adaptation of system pace, and also examined whether participants adjusted their pace towards that of the system. The study found reliable measures of user pace and showed that participants do entrain to the system’s pace. The third and fourth studies examined whether users would prefer a system that adapted its pace to the user over a system that used a static baseline pace. Results indicated that a majority of fast-paced users preferred the adaptive interface, but that slow-paced users generally preferred the static baseline interface. We discuss several design implications, including opportunities for systems to improve user experience for fast users by automatically adapting system pace to user pace.