International Journal of Human-Computer Studies最新文献

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.

Cervical cancer screening has the potential to save lives, but it can also produce strong anxiety and self-stigma in those who are screened. Although there has been a recent turn towards women's health in design, the potential for design to ameliorate experiences of cervical screening remains underexplored. In this paper, we report on a design research study with 15 Irish women that qualitatively unpacked their attitudes towards screening, their social learning processes, mediated through technology, and how they live with and give meaning to health-related information related to the topic of cervical screening and which they procure online. Following this, we developed NALA, a product-service-system that aimed to 1) allow self-sampling for HPV via menstrual blood, and 2) provide information around the topic of HPV, cervical cancer, and screening. This paper presents NALA, a preliminary evaluation of the system, and concludes with provocations for continuing design research in the area of digital design for women's health.

A frequent use of conversational user interfaces (CUIs) today is improving the users’ experience with online quantitative surveys. In this paper, we explore the use of CUIs in qualitative surveys. As a concrete use case, we adopt a specific, well-structured, qualitative research method called the repertory grid technique (RGT). We developed a hybrid user interface (HUI) that combines a graphical user interface (GUI) with a CUI to automate the distinct stages in a RGT survey. A pilot study was used to verify the feasibility of the approach and to fine-tune interface aspects of an initial prototype. In this paper, we report the results of a within-subject lab experiment with 24 participants that aimed to establish the performance and UX in a realistic context of a more advanced prototype. We observed a small decrease in UX in some hedonistic aspects, but also confirmed that the HUI performs similarly to a human agent in most pragmatic aspects. These results provide support for our hypothesis that automating qualitative surveys is possible with proper interface design. We hope that our work can inspire other researchers to design additional tools for qualitative survey automation, especially now that generative AI systems, such as ChatGPT, open up interesting new ways for computer systems to interact with users in natural language.

The main objective of the current study was to test the efficiency of adaptive cognitive training programs based on human-computer interaction. More specifically, the influence of this training on resistance to orientation visual illusions (Poggendorff, Zӧllner) and metric visual illusions (Ebbinghaus, Müller-Lyer, Ponzo) was tested. In addition, the second goal of the study was to verify whether Witkin's field dependence/independence, defined as an individual's ability to identify parts of an organized visual field as elements separate from that field, moderates the influence of cognitive training on visual illusion resistance. 250 participants aged 19–32 took part in the experiment. In addition to a passive control group, three training groups were used: a working memory-training group, an attention-training group, and a perception-training group. The groups were homogeneous in terms of gender, age, and proportion of field-dependent and field-independent individuals. All groups received about three weeks of adaptive cognitive training, consisting of 18 sessions of 30 min per day. The results showed that, in general, field-dependent participants appeared to be more susceptible to visual illusions than field-independent ones. Most importantly, working memory training appeared to be effective in reducing susceptibility to the Ponzo illusion.

In both remote and physical work environments, it is commonplace for help-seeking messages to be rejected by other colleagues. This paper investigates how signifying co-workers’ stress status would influence the social diction and empathy of help-seekers in the context of rejection. 36 participants were recruited to perform help-seeking tasks with virtual co-workers via a professional mobile messaging application (Trillian). Their device was tailored with a vibrotactile mechanism (TacStatus), which could signify different emotional states of the co-workers: no-cue, relaxed, normal, and stressful. Independent sample Friedman nonparametric tests were conducted to analyze the social diction and empathy of the participants in their messages for help-seeking and responses to the co-workers’ rejection. This study revealed that stress cues have observable impacts on the social diction and empathy of help-seekers. Stressful and relaxed cues were found to evidently shape the social diction of help-seekers. When faced with a relaxed co-worker, the help-seeker felt disappointed and unaccepted after being rejected. By contrast, when confronted with a stressful cue, help-seekers tended to exhibit relatively more positive emotions after been rejected. This study attempts to reveal the mechanism through which stress cues influence professional messaging interactions and collaboration. The findings could provide implications for the design of socio-emotional cues in the context of messaging.

As more automation is integrated into vehicles, understanding how humans interact with these new technologies is becoming increasingly important given the high cost of errors. Cognitive models have the potential to provide insights into human-automated vehicle interaction and inform risk assessment, user interface design, and risk mitigation interventions. We argue that accounting for individual differences is necessary in order to derive the full benefits of cognitive models. We describe several methods for modeling individual differences and demonstrate potential pitfalls of using a one-size-fits-all model. In addition, we explain how modeling individual differences is important for risk assessment, designing robust user interfaces and automated systems, and designing effective risk mitigation interventions. Finally, we use a simulation study to demonstrate possible benefits of modeling individual differences in unmanned vehicle management.

Time-bounded collaborative events bring together participants from different backgrounds to address a problem by creating a computational artifact over a short period of time (e.g., one or two days). Examples of these events include hackathons, game jams, codefests, ideathons, etc. Time-bounded events present minimal restrictions for participation, with hundreds of people attending an increasing number of events each year. There are different reasons why people decide to participate in time-bounded collaborative events. While several studies focused on studying the motivations to participate in these events, there is no consensus on how to measure this motivation. This paper aims to develop and validate a measure of motivation that addresses the participants’ willingness to attend a time-bounded collaborative event. The construction process of our scale used quantitative and qualitative analysis techniques. Our model of motivation is composed of a set of 20 questions that are grouped into five sub-constructs that measure different motivations: Technical, Personal Curiosity, Personal Ideation, Social Teamwork, and Business Connections. Our results show that our measure has internal reliability and convergent and discriminant validity. Our findings contribute to a broader understanding of the motivations for participating in time-bounded collaborative events and provide some implications for the research and practice of such events.