Benefits and costs of adaptive user interfaces

The paper examines the positive and the possible adverse effects of adaptive user interfaces (AUIs) in the context of an in-vehicle telematic system as a function of four factors: (1) four different levels of adaptivity (ranging from manual to fully adaptive with intermediate levels); (2) different tasks; (3) routine (familiar) and non-routine (unfamiliar) situations; and (4) different user age groups. Both experiments included three sessions during which participants drove a simple driving simulator and performed tasks with the telematic system at one of the adaptivity levels. We measured task performance times and lane position variance. Adaptivity was not always equally beneficial, and its benefits depended on a number of factors, including the frequency in which the tasks were performed, the user’s age, the difficulty of the task and the user's involvement in the task. In familiar, routine situations, a fully adaptive system was beneficial for all participants, particularly older ones. In unfamiliar situations, to which the AUI was not adjusted, cognitive workload increased substantially, adversely affecting performance. Intermediate levels of adaptivity keep users involved in the task and help them become more proficient when performing both routine and non-routine tasks. However, intermediate levels of adaptivity should also be implemented with care, because they may also have adverse effects when users encounter non-routine situations.     

A User Study of Visualization Effectiveness Using EEG and Cognitive Load

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.     

Virtual user models for the elderly and disabled for automatic simulated accessibility and ergonomy evaluation of designs

This paper presents a framework for automatic simulated accessibility and ergonomy testing of virtual prototypes of products using virtual user models. The proposed virtual user modeling framework describes virtual humans focusing on the elderly and people with disabilities. Geometric, kinematic, physical, behavioral and cognitive aspects of the user affected by possible disabilities are examined, in order to create virtual user models able to represent people with various functional limitations. Hierarchical task and interaction models are introduced, in order to describe the user’s capabilities at multiple levels of abstraction. The use of alternative ways of a user task’s execution, exploiting different modalities and assistive devices, is supported by the proposed task analysis. Experimental results on the accessibility and ergonomy evaluation of different workplace designs for the use of a telephone and a stapler show how the proposed framework can be put into practice and demonstrate its significant potential.     

Effects of time affordance and operation mode on a smart microwave oven touch-sensitive user Interface design

Smart homes have revolutionized our daily lives. With today’s fast-paced lifestyle, pursuing a high quality of life has become many people’s goal and motivation. The purpose of this study is to investigate the user interface design of smart microwave ovens pertinent to time affordance and operation mode. A 2?×?3 mixed factorial design was planned to help explore whether different time affordances (i.e., high and low) and operation modes (i.e., traditional, touch, and smart) may affect users’ task performance and subjective evaluation. Using the convenience sampling method, 24 adults were recruited to participate in the experiment. The experimental data were collected pertinent to task performance, the system usability scale, and through questionnaires created using a 7-point Likert scale, and semi-structured interviews. The generated results revealed that: (1) there was a significant difference in the time affordance. Multiple time information cues can help reduce uncertainty, providing high time affordance to the participants. (2) There were significant differences among different operation modes. A simple and intuitive “smart” type is in line with user expectations. (3) The overall analysis of task performance and satisfaction consistently showed that high time affordance is better than low time affordance in all aspects, and the “smart” type had the best task performance. (4) The user interface design should be followed by users’ experience and the features of the touch product. Partially smart and custom function adjustments may effectively improve the user’s control of smart products.

     

Effects of etiquette strategy on human–robot interaction in a simulated medicine delivery task

The objective of this study was to examine the extent to which a model of linguistic etiquette in human–human interaction could be applied to human–robot interaction (HRI) domain, and how different etiquette strategies proposed through the model might influence performance of humans and robots as mediated by manipulations of robot physical features, in a simulated medicine delivery task. A “wizard of Oz” experiment was conducted in which either a humanoid robot or a mechanical-looking robot was used to present medicine reminding utterances (following different etiquette strategies) to participants, who were engaged in a primary cognitive task (a Sudoku puzzle). Results revealed the etiquette model to partially extend to the HRI domain. Participants were not sensitive to positive language from robots (e.g., appreciation of human values/wants) and such a strategy did not succeed in supporting or enhancing the “positive face” of human users. Both “bald” (no linguistic courtesy) and mixed strategies (positive and “negative face” (minimizing user imposition) saving) resulted in moderate user perceived etiquette scores (PE). However, individual differences suggested such robot linguistic strategies should be applied with caution. Opposite to this, a negative face saving strategy (supporting user freedom of choice) promoted user task and robot performance (in terms of user response time to robot requests), and resulted in the highest PE score. There was also evidence that humanoid robot features provide additional social cues that may be used by patients and support human and robot performance, but not PE. These results provide a basis for determining appropriate etiquette strategies and robot appearance to promote better collaborative task performances for future health care delivery applications of service robots.     

Prediction of individual learning curves across information visualizations

Confident usage of information visualizations is thought to be influenced by cognitive aspects as well as amount of exposure and training. To support the development of individual competency in visualization processing, it is important to ascertain if we can track users’ progress or difficulties they might have while working with a given visualization. In this paper, we extend previous work on predicting in real time a user’s learning curve—a mathematical model that can represent a user’s skill acquisition ability—when working with a visualization. First, we investigate whether results we previously obtained in predicting users’ learning curves during visualization processing generalize to a different visualization. Second, we study to what extent we can make predictions on a user’s learning curve without information on the visualization being used. Our models leverage various data sources, including a user’s gaze behavior, pupil dilation, and cognitive abilities. We show that these models outperform a baseline that leverages knowledge on user task performance so far. Our best performing model achieves good accuracies in predicting users’ learning curves even after observing users’ performance on a few tasks only. These results represent an important step toward understanding how to support users in learning a new visualization.     

Using Task-Induced Pupil Diameter and Blink Rate to Infer Cognitive Load

Minimizing user cognitive load is suggested as an integral part of human-centered design, where a more intuitive, easy to learn, and adaptive interface is desired. In this context, it is difficult to develop optimal strategies to improve the design without first knowing how user cognitive load fluctuates during interaction. In this study, we investigate how cognitive load measurement is affected by different task types from the perspective of the load theory of attention, using pupil diameter and blink measures. We induced five levels of cognitive load during low and high perceptual load tasks and found that although pupil diameter showed significant effects on cognitive load when the perceptual load was low, neither blink rate nor pupil diameter showed significant effects on cognitive load when the perceptual load was high. The results indicate that pupil diameter can index cognitive load only in the situation of low perceptual load and are the first to provide empirical support for the cognitive control aspect of the load theory of attention, in the context of cognitive load measurement. Meanwhile, blink is a better indicator of perceptual load than cognitive load. This study also implies that perceptual load should be considered in cognitive load measurement using pupil diameter and blink measures. Automatic detection of the type and level of load in this manner helps pave the way for better reasoning about user internal processes for human-centered interface design.     

基于多维特征交叉的深度协同过滤算法

基于深度学习的推荐算法最初以用户和物品的ID信息作为输入,但是ID无法很好地表现用户与物品的特征。在原始数据中,用户对物品的评分数据在一定程度上能表现出用户和物品的特征,但是未考虑用户的评分偏好以及物品的热门程度。在评分任务中使用隐式反馈和ID信息作为用户与物品的特征,在消除用户主观性对特征造成的噪声的同时在一定程度上缓解冷启动问题,利用单层神经网络对原始高维稀疏特征降维,使用特征交叉得到用户与物品的低阶交互,再利用神经网络捕获用户与物品的高阶交互,有效提取了特征间的高低阶交互。在四个公开数据集上的实验表明,该算法能有效提高推荐精度。     

Automatic personality identification using writing behaviours: an exploratory study

The ability of automatically identifying users’ personality is an important part of building adaptive systems and providing personalised services. However, there is still a lack of evaluation methods which can not only unobtrusively gather user data without supplement equipment, but also provide accurate and real-time prediction of users’ personality. This paper presents a new approach to identifying personality by combining writing features and machine learning techniques. We conducted an exploratory study where we collected participants’ handwriting information and personality information via questionnaire. From these data, we extracted writing features and created classifiers for seven personality dimensions. Our top results include a unique set of writing features which could be predictive of personality and binary classifiers for the seven personality dimensions, with accuracies ranging from 62.5% to 83.9%. These results show that writing features are useful for personality identification when suitable classifiers are adopted.     

Beyond performance: Feature awareness in personalized interfaces

Personalized graphical user interfaces have the potential to reduce visual complexity and improve interaction efficiency by tailoring elements such as menus and toolbars to better suit an individual user's needs. When an interface is personalized to make useful features more accessible for a user's current task, however, there may be a negative impact on the user's awareness of the full set of available features, making future tasks more difficult. To assess this tradeoff we introduce awareness as an evaluation metric to be used in conjunction with performance. We then discuss three studies we have conducted, which show that personalized interfaces tradeoff awareness of unused features for performance gains on core tasks. The first two studies, previously published and presented only in summary, demonstrate this tradeoff by measuring awareness using a recognition test of unused features in the interface. The studies also evaluated two different types of personalized interfaces: a layered interfaces approach and an adaptive split menu approach. The third study, presented in full, focuses on adaptive split menus and extends results from the first two studies to show that different levels of awareness also correspond to an impact on performance when users are asked to complete new tasks. Based on all three studies and a survey of related work, we outline a design space of personalized interfaces and present several factors that could affect the tradeoff between core task performance and awareness. Finally, we provide a set of design implications that should be considered for personalized interfaces.     

动态调整用户认知负荷的智能增强现实系统

把认知负荷测量技术集成进空间增强现实系统,提出动态调整用户认知负荷水平的智能空间增强现实系统（ISARS）系统。ISARS的目标是对用户在进行任务时的认知状态进行实时监测,并根据用户认知状态动态调整空间增强现实系统的增强信息,以使用户处于最佳工作状态并提高工作效率和保证工作质量。ISARS包含两个子系统：空间增强现实（SAR）子系统和认知负荷度量（CLM）子系统,SAR子系统把不同的增强信息投影到物体物理表面。CLM子系统对用户在工作过程中的认知负荷进行度量。实验结果表明,ISARS能够根据用户当前认知负荷水平对任务相关的要素进行自适应调整以更好适合用户实际工作能力,从而提高用户工作效率。     

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.     

深度程序理解视角下代码搜索研究综述

代码搜索任务旨在通过分析用户需求,结合用户意图来找到满足其需求的软件构件。在加强软件复用性的同时,提高软件开发维护效率,降低成本。与传统的文档检索不同,程序特性往往隐含在标识符和代码结构中,理解程序功能是实现高效代码搜索的关键。从深度程序理解视角切入对代码搜索任务进行定义,并总结梳理近期代码搜索研究进展。针对当前代码搜索研究评估方法和数据集进行了整理。针对研究中存在的问题,对未来代码搜索研究进行展望,为后来研究者提供参考。     

Role of cognitive and functional performance in the interactions between elderly people with cognitive decline and an avatar on TV

The complexity of new information technologies (IT) may limit the access of elderly people to the information society, exacerbating what is known as “the digital divide,” as they appear to be too challenging for elderly citizens regardless of the integrity of their cognitive status. This study is an attempt to clarify how some cognitive functions (such as attention or verbal memory) may determine the interaction of cognitively impaired elderly people with technology. Twenty participants ranging from mild cognitive impairment to moderate Alzheimer’s disease were assessed by means of a neuropsychological and functional battery and were asked to follow simple commands from an avatar appearing on a TV by means of a remote control, such as asking the participant to confirm their presence or to respond Yes/No to a proposal to see a TV program. The number of correct answers and command repetitions required for the user to respond were registered. The results show that participants with a better cognitive and functional state in specific tests show a significantly better performance in the TV task. The derived conclusion is that neuropsychological assessment may be used as a useful complementary tool for assistive technology developers in the adaptation of IT to the elderly with different cognitive and functional profiles. Further studies with larger samples are required to determine to what extent cognitive functions can actually predict older users’ interaction with technology.     

The role of user-centered AR instruction in improving novice spatial cognition in a high-precision procedural task

AR instruction is a kind of virtual information presented on a human–computer interface. It allows users to view the geometric state, spatial relationship, operation method, and other information involved in the physical task, to form the spatial cognition of the current interaction process. At present, AR instructions cannot support high-precision procedural tasks. The reason is that the existing research work is to use visual elements to express the spatial relationship of physical tasks, without considering transforming the long-term accumulated potential experience of advanced users into a series of effective visual features and interaction modes, to promote the new users to quickly conceive the task intention. In this paper, a user-centered AR instruction(UcAI) is defined and tested for the first time in a procedural task. The control experiment and behavior analysis of 30 participants designing two tasks with different operation precision show that UcAI is more beneficial to improve the user's spatial cognitive ability than conventional AR instruction. Especially in the high-precision operation task, UcAI plays an important role. Our research results have a certain guiding significance for advanced AR instruction design, which extends AR technology to physical tasks with high cognitive complexity.     

Reduced GUI for an interactive geometry software: Does it affect students' performance?

PurposeThe purpose of this paper is to describe an experimental study to reduce cognitive load and enhance usability for interactive geometry software.Design/methodology/approachThe Graphical User Interface is the main mechanism of communication between user and system features. Educational software interfaces should provide useful features to assist learners without generate extra cognitive load. In this context, this research aims at analyzing a reduced and a complete interface of interactive geometry software, and verifies the educational benefits they provide. We investigated whether a reduced interface makes few cognitive demands of users in comparison to a complete interface. To this end, we designed the interfaces and carried out an experiment involving 69 undergraduate students.FindingsThe experimental results indicate that an interface that hides advanced and extraneous features helps novice users to perform slightly better than novice users using a complete interface. After receiving proper training, however, a complete interface makes users more productive than a reduced interface.Originality/valueIn educational software, successful user interface designs minimize the cognitive load on users; thereby users can direct their efforts to maximizing their understanding of the educational concepts being presented.     

Skill-based human–robot cooperation in tele-operated path tracking

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operator’s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of user’s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking.     

脑机接口辅助的动态目标选择技术

动态目标选择是现代交互界面中最为基础的交互任务之一,目前存在多种辅助技术,但这些技术的决策和参数设定有很强的经验性,无法根据用户的当前状态进行调整.为了解决这个问题,基于两个认知负荷与难度感知的假设,提出一种脑机接口辅助的动态目标选择技术,利用近红外光谱信号对用户认知负荷感知的敏感性,实时地调整目标选择技术参数,给不同用户个体提供个性化辅助,适用于不同场景、用户状态和任务难度.通过一组实验,对提出的假设进行了验证,并且基于该假设构建的脑机接口辅助的动态目标选择技术,较不作任何辅助和固定辅助技术两种方案都更优,具体地,在选择错误率上分别降低20.55%和12.09%,在完成时间上分别降低998.35ms和208.67ms.