International Journal of Human-Computer Studies最新文献

Prior studies of human communication have demonstrated that prosocial outcomes occur when facets of communication converge between interlocutors—for example, social likeability and perceived competence increase when people adapt their pitch or rate of speech towards one another. Inspired by these findings, we examined whether user preference for fast or slow system speech rate covaries with user speech rate. Experimental participants uploaded samples of their reading speech and spontaneous speech, and then listened to fast and slow system speech before choosing their preferred system speech rate. Results aligned with our hypothesis that fast speakers prefer faster system speech and that slow speakers prefer slow. Design implications are discussed, as well as plans for future studies that explore the potential for systems to automatically measure the user’s speech rate and converge towards it.

For the foreseeable future, automated vehicles (AVs) will coexist on the roads with human drivers. To avoid accidents, AVs will require knowledge on how human drivers typically make high-stakes and time-sensitive decisions (e.g., whether or not to brake). Providing such insights could be statistical models designed to explain human information processing and decision making. This paper attempts to address a roadblock that prevents one class of such "cognitive models", evidence accumulation models (EAMs), from being widely applied in the design of AV systems: their high demands for data. Specifically, we investigate whether Bayesian hierarchical modeling can be used to determine a person's characteristics, if we only have limited data about their behavior but extensive data on other (comparable) people's behaviors. Leveraging a simulation study and a reanalysis of experimental data, we find that most parameters of Decision Diffusion Models (a class of EAMs) – representing information processing components – can be adequately estimated with as few as 20 observations, if prior information regarding the decision-making processes of the population is incorporated. Subsequently, we discuss the implications of our findings for the modeling of traffic situations.

Touchscreen is the primary solution to interact with public devices such as Automated Teller Machines (ATMs). However, the touch modality raises health concerns since users have to touch the screens, and therefore risking the spread of contagious diseases. We design PalmSpace, an alternate input technique leveraging users’ hand palms to interact with public devices. With Palmspace, UI elements are mapped onto the users’ palms and can be accessed by touching various locations directly on the palm. We conduct a series of user studies to evaluate several design options, such as interface layout, item size, preferred item location, and suitable feedback for items. Based on the results, we design PalmSpace and compare its performance with mid-air input. We show that PalmSpace is a potential solution to interact with public devices without using their touchscreen. We conclude with design guidelines for using the palm as an alternative input space for touchscreen devices.

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system ($p$ $<$ 0.01). Finally, we report an interesting relationship between usability, alignment, and perceived intelligence wherein greater perceived alignment of recommendations with one’s own preferences leads to higher perceived intelligence ($p$ $<$ 0.01) and higher usability ($p$ $<$ 0.01).

A growing body of human–robot interaction literature is exploring whether and how social robots, by utilizing their physical presence or capacity for verbal and nonverbal behavior, can influence people’s moral behavior. In the current research, we aimed to examine to what extent a social robot can effectively encourage people to act honestly by offering them moral advice. The robot either offered no advice at all or proactively offered moral advice before participants made a choice between acting honestly and cheating, and the underlying ethical framework of the advice was grounded in either deontology (rule-focused), virtue ethics (identity-focused), or Confucian role ethics (role-focused). Across three studies ($N=1,693$), we did not find a robot’s moral advice to be effective in deterring cheating. These null results were held constant even when we introduced the robot as being equipped with moral capacity to foster common expectations about the robot among participants before receiving the advice from it. The current work led us to an unexpected discovery of the psychological reactance effect associated with participants’ perception of the robot’s moral capacity. Stronger perceptions of the robot’s moral capacity were linked to greater probabilities of cheating. These findings demonstrate how psychological reactance may impact human–robot interaction in moral domains and suggest potential strategies for framing a robot’s moral messages to avoid such reactance.

With the growing concern for the health of ageing populations, much research continues to look at the impact of cognitive training, particularly in relation to cognitive decline. We sought to use novel techniques, including augmented reality and portable neurotechnology, to evaluate the impact of a dynamically adjusting cognitive training programme, in comparison to a statically challenging alternative. Before and after an 8-week training period, and at a 5-week follow-up, we used portable functional Near Infrared Spectroscopy to examine mental workload in a mixed battery of cognitive and transfer tasks. A recently developed tablet-based task was used to identify changes in cognitive misbinding. Augmented Reality was used to create a supermarket shopping experience, as a more ecologically valid and realistic transfer task relating to an everyday task relating to independence that quickly becomes difficult with cognitive decline. The analyses showed a decreased mental workload within the dorsolateral prefrontal cortex and that participants considerably increased their performance in the trained task. Some results were maintained at the 5-week follow-up assessment. In terms of transfer, we observed reliable group differences immediately after training completion, which were mainly driven by distinct conditions. Some behavioural memory gains were maintained during the follow-up. The use of novel technologies brought new insights into the effects produced by the dynamic computerised cognitive training programme, which has potential future applications in cognitive decline screening and prevention.

Gamification is finding growing application in the field of physical activity, promising engaging and motivating experiences that foster behavioural change. Yet, rigorous empirical work substantiating favourable claims is limited. Our study sought to find evidence for the effects resulting from gamification of physical activity on the users’ motivation, perceived usefulness, and the behavioural change in terms of step counts. We employed a four-week randomised controlled field experiment to investigate how the choice of different gamification designs affects outcomes. Participants were provided with a wearable physical activity tracker and randomly assigned to either a: 1) competitive gamified group; 2) cooperative gamified group; 3) hybrid gamified group; or 4) control group. Data gathered includes a panel dataset of step counts and self-reported data on the users’ intrinsic motivation and perceived usefulness. We found that at the end of the intervention, gamification made no difference to self-assessed intrinsic motivation or perceived usefulness compared to a non-gamified self-tracking experience. Yet, despite the lack of psychological effects, the use of gamification did result in stronger behavioural outcomes relative to the control group - in the form of increased step counts. Indeed, all groups treated with gamification recorded an increase in step counts during the intervention period. Furthermore, amongst the gamified treatments, it was the hybrid design that generated the largest difference in step counts (relative to the control group). The finding that gamification can stimulate a stronger behavioural outcome, but does not evoke a stronger psychological outcome at the end of the intervention merits further investigation as to the mechanisms at play.

Tablets are often used as a distant input device to interact with large displays. Users can hold the tablet with two hands when interacting for a long time to avoid fatigue or dropping it. Still, most previous distant interaction techniques consider only one-handed tablets. This paper presents novel interaction techniques to perform distant pointing and command selection tasks using both thumbs while holding the device with two hands. In a first step, we design and evaluate six interaction techniques for distant pointing, based on using one or two thumbs, absolute or relative cursor control and single- or dual-precision pointing. Our results show that a combination of dual-thumb and relative pointing is preferred and more efficient than any other dual-thumb or single-thumb technique we designed. In a second step, we focus on command selection using two thumbs. We designed seven dual-thumb menu techniques based on quarter-pie or half-pie layouts, offering from 64 to 900 items. Our results show that using quarter-pie menu techniques is more efficient, usable and appreciated than using a linear or half-pie menu. Furthermore, we show that augmenting the number of items for quarter-pie and half-pie menu techniques has a low impact on command selection performance. In a third step, we compare our quarter-pie menus to bimanual marking menus. Our results show that quarter-pie menus are more efficient and less error-prone. We finally provide a set of lessons for designing efficient dual-thumb interaction techniques for two-handed tablets.

Gaze-based writing is one of the most widespread eye tracking applications for human–computer interaction. While eye tracking communication has traditionally been employed as an assistive technology, declining prices of eye trackers now make it a feasible alternative to keyboards or touchscreens in many contexts (for example, the interaction with public info points). In this paper we propose Leyenes, a text entry method based on smooth pursuit, a natural eye movement that occurs when the gaze follows a moving target. Our approach requires no explicit calibration by the user, allowing for more spontaneous interaction and enabling eye input even when calibration is difficult to achieve or maintain. To the best of our knowledge, Leyenes is the first text entry technique based on smooth pursuit that considers both (approximate) gaze speed and position and employs a linear interface instead of the more common circular layouts. The results of the user study we conducted show that the proposed solution is slow but robust, with a very low error rate, which makes it particularly suitable for extemporaneous writing of short text.