Empathy is increasingly recognized as a key factor in human–AI communication, yet conventional approaches to “digital empathy” often focus on simulating internal, human like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we propose a human-centered taxonomy that emphasizes observable empathic behaviors and introduce a new dataset, SENSE-7, of real-world conversations between information workers and Large Language Models (LLMs), which includes per-turn empathy annotations directly from the users, along with user characteristics, and contextual details, offering a more user-grounded representation of empathy. Analysis of 695 conversations from 109 participants reveals that empathy judgments are highly individualized, context-sensitive, and vulnerable to disruption when conversational continuity fails or user expectations go unmet. To promote further research, we provide a subset of 672 anonymized conversation and provide exploratory classification analysis, showing that an LLM-based classifier can recognize 5 levels of empathy with an encouraging average Spearman ρ = 0.369 and Accuracy = 0.487 over this set. Overall, our findings underscore the need for AI designs that dynamically tailor empathic behaviors to user contexts and goals, offering a roadmap for future research and practical development of socially attuned, human-centered artificial agents.

Client-Service Representatives (CSRs) are vital to organizations.Frequent interactions with disgruntled clients, however, disrupt their mental well-being.To help CSRs regulate their emotions while interacting with uncivil clients, we designed Care-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use.Our comparative analyses between 665 human and Care-Pilotgenerated support messages highlight Care-Pilot's ability to adapt to and demonstrate empathy in various incivility incidents.Additionally, 143 CSRs assessed Care-Pilot's empathy as more sincere and actionable than human messages.Finally, we interviewed 20 CSRs who interacted with Care-Pilot in a simulation exercise.They reported that Care-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support.Yet, they also noted deployment challenges and emphasized the indispensability of shared experiences.We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants for worker mental health.

Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

Behind the scenes of maintaining the safety of technology products from harmful and illegal digital content lies unrecognized human labor. The recent rise in the use of generative AI technologies and the accelerating demands to meet responsible AI (RAI) aims necessitates an increased focus on the labor behind such efforts in the age of AI. This study investigates the nature and challenges of content work that supports RAI efforts, or "RAI content work," that spans content moderation, data labeling, and red teaming -- through the lived experiences of content workers. We conduct a formative survey and semi-structured interview studies to develop a conceptualization of RAI content work and a subsequent framework of recommendations for providing holistic support for content workers. We validate our recommendations through a series of workshops with content workers and derive considerations for and examples of implementing such recommendations. We discuss how our framework may guide future innovation to support the well-being and professional development of the RAI content workforce.

The future of work is rapidly changing, with remote and hybrid settings blurring the boundaries between professional and personal life. To understand how work rhythms vary across different work settings, we conducted a month-long study of 65 software developers, collecting anonymized computer activity data as well as daily ratings for perceived stress, productivity, and work setting. In addition to confirming the double-peak pattern of activity at 10:00 am and 2:00 pm observed in prior research, we observed a significant third peak around 9:00 pm. This third peak was associated with higher perceived productivity during remote days but increased stress during onsite and hybrid days, highlighting a nuanced interplay between work demands and work settings. Additionally, we found strong correlations between computer activity, productivity, and stress, including an inverted U-shaped relationship where productivity peaked at around six hours of computer activity before declining on more active days. These findings provide new insights into evolving work rhythms and highlight the impact of different work settings on productivity and stress.

We present AImagery, an AI-powered immersive relaxation experience tailored to user preferences and physiological feedback. In a study with 32 participants, half of them experienced a multisensory experience with scent, biofeedback, and a personalized AI audio story based on their heart rate, self-reported mood and custom scenery. The experimental group showed a significant anxiety reduction for those with moderate to high anxiety, as opposed to the control group (non-guided meditation/baseline resting control condition). User feedback was positive and received high ratings for enjoyment, immersion and, to a lesser extend, sleepiness. Our results highlight the potential of multisensory AI-driven relaxation tools for those with elevated anxiety.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

An oft-repeated ideal of personal information management (PIM) is to have “the right information, at the right time, in the right place…” for the current need. But the technologies and innovations that bring us ever closer to this ideal carry costs as well as benefits. In this ninth in a series of PIM workshops, we give closer, critical consideration to the “right time, right place” ideal of PIM. Can we manage the potential downsides involved in achieving this ideal, while preserving its obvious benefits? Or should we revise our ideal of PIM?

As concerns about employee burnout and skilled staff shortages in cybersecurity grow, our study aims to better understand the contributing factors to burnout in this field. Utilizing a mixed-methods approach, we analyze self-reported job and personal characteristics, along with digital activity data from 35 incident responders, identifying several factors such as high workload, time pressure, and lack of support from management. Our findings reveal that over half of the participants experience burnout (N=19), which is linked to increased workload, limited control, poor teamwork, and inadequate recognition. Burned-out responders often work more than 40 hours per week, have poor sleep quality, and engage in more email activities, meetings, and after-hour collaborations. Through our research, we also identify coping strategies individuals use to mitigate these stressors. Based on our findings, we provide practical recommendations to help organizations better support their cybersecurity incident response teams. While our study acknowledges limitations and suggests future research directions, it contributes significantly to understanding the challenges faced by cybersecurity incident responders. Our insights offer a comprehensive understanding of burnout factors in this domain and have broader implications for other high-stress work environments consistent with the interdisciplinary nature of CSCW.

The global COVID-19 pandemic has spurred on new collaborations across borders, and emphasized the importance of supporting wellbeing in the workplace, whether that workplace is hybrid, remote, or in-person. Work in CSCW, HCI, and organizational psychology has explored how people come to understand their wellbeing at work, and the role of identity, culture, and organizational factors in that process. In this study, we build on this past research and explore the importance of these factors when designing tools that support worker wellbeing for location-independent teams. We ask the question: how did organizational, cultural, and individual factors influence how workers understood their workplace wellbeing needs during the move to remote work? To investigate this question, we conduct a large scale linguistic analysis of 13,265 diary entries collected between 2020 - 2022, and complement it with in-depth interviews with 26 global employees, exploring intersections between technology, context, and wellbeing needs. We utilize this data to analyze the broader human infrastructure supporting hybrid and remote work, demonstrating how ideas around wellbeing are influenced by the (often technology-mediated) environment around both information and essential workers, and power differentials within it. Building on our findings, we provide recommendations for how technology design can better support more diverse and inclusive forms of worker wellbeing.

Work-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and limited resources within organizational and interpersonal contexts. These challenges are compounded by technologies that blur the boundaries of work and nonwork in the always-on work cultures. At an individual level, such challenges can be subsided through the effective application of self-regulation techniques, such as implementation intentions and mental contrasting (IIMC). Further supporting these techniques through reflection on personal data, we implement the idea of data-driven IIMC into a self-tracking and behavior planning system and evaluate it in a three-week between-participant study with 43 information workers who used our system for improving work-nonwork balance. We find evidence that reflection on personal data improves awareness of behavior plan compliance and rescheduling, which are important in realizing work-nonwork balance goals. We also observe the value of micro-reflection, reflection on limited data of the very recent past, for IIMC. Our findings highlight opportunities for automation in data collection and sense-making and for further exploring the role of data-driven IIMC as boundary negotiating artifacts in support of work-nonwork balance goals.

Previous research suggests that biomusic, a type of biosignal sharing, is effective at promoting empathy and closeness among individuals. However, it is unclear whether these effects are due to the information it encodes or other emotional aspects of its resulting music. To explore this question, we developed a Generative Adversarial Network (GAN) to create synthetic biomusic that approximates real biomusic, and employed deception to evaluate its effects on 24 pairs of participants engaged in real-time emotional disclosure. Users reported that both real and synthetic biomusic provided the same amount of information about their conversational partner as observing body language, facial expressions, or vocal tone. Further, both conditions increased users’ ratings of closeness and empathy with each other compared to listening to no music. However, we found no statistically significant differences between the two biomusic conditions across any of our metrics. We discuss the implications of these results for the design of future biomusic systems.

Information workers often struggle to balance their time for a variety of activities like focused work, communication, and caring. This study analyzes the impact of a commercially available computer-assisted time protection intervention that automatically and preemptively schedules calendar time for self-determined activities. We analyzed the behaviors and self-reports of workers in two naturalistic studies. First, we studied 27 workers who were already using Computer-Assisted Protected Time (CAP time) and found that they mainly used it for focused work. Second, we analyzed the effect of CAP time as a randomized intervention on 89 workers who never had CAP time and found that those with it self-reported an increase in performance, job resources, and immersion. In both studies, workers with CAP time exhibited a rearrangement of activities leading to an overall reduction in work activity. This study highlights new opportunities for intelligent time-management interventions and the importance of protected time at work.

Deployment of facial action unit recognition models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth generalization analysis across several sources of variance: individuals (40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we propose using self-supervised denoising autoencoders to transfer facial expressions of different people onto a common facial template which is then used to train and evaluate each of the models. We show that person-independent models yielded significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3 %), leading to a generalization gap of 5.3%. However, normalizing the data with the proposed method significantly increased the performance of person-independent models (59.6%). Similarly, the proposed method was able to significantly reduce the generalization gap when considering gender (2.4%), skin type (5.3%), and dataset (9.4%). These findings represent an important step towards the creation of more generalizable facial action unit recognition systems.

Workplace stress has been increasing in recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical expenses. An essential step toward managing stress involves finding comfortable ways to sense workers and recognizing stress as soon as it happens. This work explores the potential value of using pervasive sensors such as keyboards, webcams, and behavioral data such as calendar and e-mail activity to passively assess individual stress levels of work in real-life. In particular, we collected a large corpus of such data from 46 remote information workers over one month and asked them to self-report their stress levels and other relevant factors several times a day. Analysis of the data demonstrates that passive sensors can effectively detect both triggers and manifestations of workplace stress and that having access to prior data of the worker is critical for developing well-performing stress recognition models. Furthermore, we provide qualitative feedback capturing workers' preferences in workplace stress monitoring.

Workplace stress-reduction interventions have produced mixed results due to engagement and adherence barriers. Leveraging technology to integrate such interventions into the workday may address these barriers and help mitigate the mental, physical, and monetary effects of workplace stress. To inform the design of a workplace stress-reduction intervention system, we conducted a four-week longitudinal study with 86 participants, examining the effects of intervention type and timing on usage, stress reduction impact, and user preferences. We compared three intervention types and two delivery timing conditions: Pre-scheduled (PS) by users and Just-in-time (JIT) prompted by the system-identified user stress-levels. We found JIT participants completed significantly more interventions than PS participants, but post-intervention and study-long stress reduction was not significantly different between conditions. Participants rated low-effort interventions highest, but high-effort interventions reduced the most stress. Participants felt JIT provided accountability but desired partial agency over timing. We present type and timing implications.

Society has witnessed a rapid increase in the adoption of commercial uses of emotion recognition. Tools that were traditionally used by domain experts are now being used by individuals who are often unaware of the technology’s limitations and may use them in potentially harmful settings. The change in scale and agency, paired with gaps in regulation, urge the research community to rethink how we design, position, implement and ultimately deploy emotion recognition to anticipate and minimize potential risks. To help understand the current ecosystem of applied emotion recognition, this work provides an overview of some of the most frequent commercial applications and identifies some of the potential sources of harm. Informed by these, we then propose 12 guidelines for systematically assessing and reducing the risks presented by emotion recognition applications. These guidelines can help identify potential misuses and inform future deployments of emotion recognition.

Artificial agents’ smiling behaviour is likely to influence their likeability and the quality of user experience. While studies of human interaction highlight the importance of smile dynamics, this feature is often lacking in artificial agents, presenting a design opportunity. We developed a virtual motivational therapist with four smiling behaviours, varying in terms of quality and dynamism . We video-recorded experimental sessions with participants who posed as patients in a therapy session. The data were analysed combining a mix of quantitative and qualitative methods, focusing on participants’ own facial expressions during the interaction. Results suggest that the condition driven using data from a real therapist, where smiles are dynamic and occur at specific moments, is the most effective. We further discuss the particular importance of smile as a multipurpose emotional display in human-machine interaction.

Embodied conversational agents have changed the ways we can interact with machines. However, these systems often do not meet users’ expectations. A limitation is that the agents are monotonic in behavior and do not adapt to an interlocutor. We present SIVA (a Socially Intelligent Virtual Agent), an expressive, embodied conversational agent that can recognize human behavior during open-ended conversations and automatically align its responses to the conversational and expressive style of the other party. SIVA leverages multimodal inputs to produce rich and perceptually valid responses (lip syncing and facial expressions) during the conversation. We conducted a user study (N=30) in which participants rated SIVA as being more empathetic and believable than the control (agent without style matching). Based on almost 10 hours of interaction, participants who preferred interpersonal involvement evaluated SIVA as significantly more animate than the participants who valued consideration and independence.

Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people’s productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).

The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N=175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N=117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.

Video-conferencing is essential for many companies, but its limitations in conveying social cues can lead to ineffective meetings. We present MeetingCoach, an intelligent post-meeting feedback dashboard that summarizes contextual and behavioral meeting information. Through an exploratory survey (N=120), we identified important signals (e.g., turn taking, sentiment) and used these insights to create a wireframe dashboard. The design was evaluated with in situ participants (N=16) who helped identify the components they would prefer in a post-meeting dashboard. After recording video-conferencing meetings of eight teams over four weeks, we developed an AI system to quantify the meeting features and created personalized dashboards for each participant. Through interviews and surveys (N=23), we found that reviewing the dashboard helped improve attendees’ awareness of meeting dynamics, with implications for improved effectiveness and inclusivity. Based on our findings, we provide suggestions for future feedback system designs of video-conferencing meetings.

Traces of searcher behaviour, such as query reformulation or clicks, are commonly used to evaluate a running search engine. The underlying expectation is that these behaviours are proxies for something more important, such as relevance, utility, or satisfaction. Affective computing technology gives us the tools to help confirm some of these expectations, by examining visceral expressive responses during search sessions. However, work to date has only studied small populations in laboratory settings and with a limited number of contrived search tasks. In this study, we analysed longitudinal, in-situ, search behaviours of 152 information workers, over the course of several weeks while simultaneously tracking their facial expressions. Results from over 20,000 search sessions and 45,000 queries allow us to observe that indeed affective expressions are consistent with, and complementary to, existing "click-based'' metrics. On a query-level, searches that result in a short dwell time are associated with a decrease in smiles (expressions of "happiness'') and that if a query is reformulated the results of the reformulation are associated with an increase in smiling---suggesting a positive outcome as people converge on the information they need. On a session-level, sessions that feature reformulations are more commonly associated with fewer smiles and more furrowed brows (expressions of "anger/frustration''). Similarly, sessions with short-dwell clicks are also associated with fewer smiles. These data provide an insight into visceral aspects of search experience and present a new dimension for evaluating engine performance.

The ability to regulate our emotions plays an important role in our psychological and physical health. Regulating emotions influences how and when emotions are expressed. We performed a large scale, longitudinal observational study to investigate the effect of emotion regulation ability on expressed affect. We found that expression of negative affect increased throughout the day. For people who suppress emotion this increase is slower that for those who do not. For those with stronger cognitive reappraisal abilities, though not significant, there was a trend for higher positive affect and negative affect increased significantly less steeply, suggesting that they might experience more positive and less negative affect. These results reflect some of the first results based on large scale, continuous tracking of behavioral expression of emotion longitudinally. Our results demonstrate the need to carefully consider the time of day and emotion regulation ability, in addition to gender and age, when attempting to automatically infer affective states for facial behavior.

Conversational information retrieval is a relatively new and fast-developing research area, but conversation itself has been well studied for decades. Researchers have analysed linguistic phenomena such as structure and semantics but also paralinguistic features such as tone, body language, and even the physiological states of interlocutors. We tend to treat computers as social agents—especially if they have some humanlike features in their design—and so work from human-to-human conversation is highly relevant to how we think about the design of human-to-computer applications. In this article, we summarise some salient past work, focusing on social norms; structures; and affect, prosody, and style. We examine social communication theories briefly as a review to see what we have learned about how humans interact with each other and how that might pertain to agents and robots. We also discuss some implications for research and design of conversational IR systems.

Information workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.

Current research on building intelligent agents for aiding with productivity and focus in the workplace is quite limited, despite the ubiquity of information workers across the globe. In our work, we present a productivity agent which helps users schedule and block out time on their calendar to focus on important tasks, monitor and intervene with distractions, and reflect on their daily mood and goals in a single, standalone application. We created two different prototype versions of our agent: a text-based (TB) agent with a similar UI to a standard chatbot, and a more emotionally expressive virtual agent (VA) that employs a video avatar and the ability to detect and respond appropriately to users' emotions. We evaluated these two agent prototypes against an existing product (control) condition through a three-week, within subjects study design with 40 participants, across different work roles in a large organization. We found that participants scheduled 134% more time with the TB prototype, and 110% more time with the VA prototype for focused tasks compared to the control condition. Users reported that they felt more satisfied and productive with the VA agent. However, The perception of anthropomorphism in the VA was polarized, with several participants suggesting that the human appearance was unnecessary. We discuss important insights from our work for the future design of conversational agents for productivity, wellbeing, and focus in the workplace.

Conversational Agents (CAs) present many opportunities for changing how we interact with information and computer systems in a more natural, accessible way. Building on research in machine learning and HCI, it is now possible to design and test multi-turn CA that is capable of extended interactions. However, there are many ways in which these CAs can "fail" and fall short of human expectations. We systematically analyzed how five different types of conversational errors impacted perceptions of an embodied CA. Not all errors negatively impacted perceptions of the agent. Repetitions by the agent and clarifications by the human significantly decreased the perceived intelligence and anthropomorphism of the agent. Turn-taking errors significantly decreased the likability of the agent. However, coherence errors significantly positively increased likability, and these errors were also associated with positive valence via facial expressions, suggesting that the users found them amusing. We believe this work is the first to identify that turn-taking, repetition, clarification, and coherence errors directly affect users' acceptance of an embodied CA, and are worth taking note by designers of such systems during dialog configuration. We release the Agent Conversational Error (ACE) dataset, a set of transcripts and error annotations of human-agent conversations. The dataset can be found at the GITHUB link: https://github.com/deepalianeja/ACE-dataset

Past work in information-seeking conversation has demonstrated that people exhibit different conversational styles---for example, in word choice or prosody---that differences in style lead to poorer conversations, and that partners actively align their styles over time. One might assume that this would also be true for conversations with an artificial agent such as Cortana, Siri, or Alexa; and that agents should therefore track and mimic a user's style. We examine this hypothesis with reference to a lab study, where 24 participants carried out relatively long information-seeking tasks with an embodied conversational agent. The agent combined topical language models with a conversational dialogue engine, style recognition and alignment modules. We see that "style'' can be measured in human-to-agent conversation, although it looks somewhat different to style in human-to-human conversation and does not correlate with self-reported preferences. There is evidence that people align their style to the agent, and that conversations run more smoothly if the agent detects, and aligns to, the human's style as well.

The delivery of mental health interventions via ubiquitous devices has shown much promise. A conversational chatbot is a promising oracle for delivering appropriate just-in-time interventions. However, designing emotionally-aware agents, specially in this context, is under-explored. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this paper, we present the design and evaluation of EMMA (EMotion-Aware mHealth Agent) through a two-week long human-subject experiment with N=39 participants. EMMA provides emotionally appropriate micro-activities in an empathetic manner. We show that the system can be extended to detect a user's mood purely from smartphone sensor data. Our results show that our personalized machine learning model was perceived as likable via self-reports of emotion from users. Finally, we provide a set of guidelines for the design of emotion-aware bots for mHealth.

A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health/wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts.

Conversational agents have the potential to support users in many tasks. However, support for productivity and well-being in the workplace has received little attention. We present the first design of a conversational system that supports information workers with multiple work-related goals, informed by a survey of the current and potential use of conversational agents in the workplace. The goals of this research include the evaluation of using an agent for scheduling and prioritizing tasks, switching tasks, providing break reminders, dealing with social media distractions and for end of the day reflection on tasks accomplished. We deployed a chat-based intelligent agent, named Amber, in a field study with 24 information workers over the course of 6 days. We present our preliminary findings from the field study and discuss implications for the design of future workplace conversational agents.

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation.

Circadian rhythms determine daily sleep cycles, mood, and cognition. Depending on an individual's circadian preference, or chronotype (i.e.,"early birds" and "night owls"), the rhythms shift earlier or later in the day. Early birds experience circadian arousal peaks earlier in the morning than night owls. Prior work has shown that individuals are more effective at analytic tasks during their peak arousal times but are more creative during their off-peak times. We investigate if these findings hold true for small groups. We find that time of day and a group's majority chronotype impact performance on analytic and creative tasks. Physiological synchrony among group members positively predicts group satisfaction. Specifically, homogeneous groups perform worse on all tasks regardless of time of day, but they achieve greater physiological synchrony and feel more satisfied as a group. Based on these findings, we present and advocate for a temporal dimension of group diversity.

How can we create technologies to help us reflect on and change our behavior, improving our health and overall wellbeing? In this talk, I will briefly describe the last several years of work our research team has been doing in this area. We have developed wearable technology to help families manage tense situations with their children, mobile phone-based applications for handling stress and depression, as well as logging tools that can help you stay focused or recommend good times to take a break at work. The goal in all of this research is to develop tools that adapt to the user so that they can maximize their productivity and improve their health.

Emotions play a major role in how interpersonal conflicts unfold. Although several strategies and technological approaches have been proposed for emotion regulation, they often require conscious attention and effort. This often limits their efficacy in practice. In this paper, we propose a different approach inspired by self-perception theory: noticing that people are often reacting to the perception of their own behavior, we artificially change their perceptions to influence their emotions. We conducted two studies to evaluate the potential of this approach by automatically and subtly altering how people perceive their own voice. In one study, participants that received voice feedback with a calmer tone during relationship conflicts felt less anxious. In the other study, participants who listened to their own voices with a lower pitch during contentious debates felt more powerful. We discuss the implications of our findings and the opportunities for designing automatic and less perceptible emotion regulation systems.

Information workers are experiencing ever-increasing online distractions in the workplace, and software to block distractions is becoming more popular. We conducted an exploratory field study with 32 information workers in their workplace using software to block online distractions for one week. We discovered that with online distractions blocked, participants assessed their focus and productivity to be significantly higher. Those who benefited most were those who reported being less in control of their work, associated with personality traits of lower Conscientiousness and Lack of Perseverence. Unexpectedly, those reporting higher control of work experienced a cost of higher workload with online distractions blocked. Those who reported the greatest increase in focus with distractions blocked were those who were more susceptible to social media distractions. Without distractions, people with higher control of work worked longer stretches without physical breaks, with consequently higher stress. We present design recommendations to promote focus for our observed coping behaviors.

Mental health disorders are a leading cause of disability worldwide. Although evidence-based psychotherapy is effective, engagement from such programs can be low. Mobile apps have the potential to help engage and support people in their therapy. We developed Pocket Skills, a mobile web app based on Dialectical Behavior Therapy (DBT). Pocket Skills teaches DBT via a conversational agent modeled on Marsha Linehan, who developed DBT. We examined the feasibility of Pocket Skills in a 4-week field study with 73 individuals enrolled in psychotherapy. After the study, participants reported decreased depression and anxiety and increased DBT skills use. We present a model based on qualitative findings of how Pocket Skills supported DBT. Pocket Skills helped participants engage in their DBT and practice and implement skills in their environmental context, which enabled them to see the results of using their DBT skills and increase their self-efficacy. We discuss the design implications of these findings for future mobile mental health systems.

Analysis of casual chit-chat indicates that differences in conversational style---the way things are said---can significantly impact a participants» impressions of the conversation and of each other. However, prior work has not systematically analyzed how important style is in task-oriented, information-seeking exchanges of the sort we might have with a conversational search agent. We examine recordings from the MISC data set, where pairs of "users" and "intermediaries" collaborate on information-seeking tasks, and look for indications of style which can be computed at scale. We find that stylistic markers identified by Tannen in casual chat do exist in information-seeking dialogue, and that participants can be arranged along a single stylistic dimension: "considerate" to "involved". This labelling for style needs no manual intervention. Furthermore, we find that there is no clear best style; but that differences in style, previously thought to impede communication, are only a problem for shorter tasks. This result is likely due to alignment of conversational style over the course of an interaction.

Abstract: How can we create technologies to help us reflect on and change our behavior, improving our health and overall wellbeing? In this talk, I will briefly describe the last several years of work our research team has been doing in this area. We have developed wearable technology to help families manage tense situations with their children, mobile phone-based applications for handling stress and depression, as well as logging tools that can help you stay focused or recommend good times to take a break at work. The overarching goal in all of this research is to develop tools that adapt to the user so that they can maximize their productivity and improve their health.

This paper describes a micro-stress intervention system for information office workers in the workplace, their responses to the interventions and machine learning models to predict the most opportune timing for providing the interventions. We studied 30 office workers for 10 days and examined their work patterns by monitoring their computer and application usage, sleep, activity, heart rate and its variability, as well as the history of micro-stress interventions provided through our desktop software. We analyzed temporal patterns of stress intervention acceptance/rejection and the relationships between their subjective and objective responses to the interventions and perceived work engagement, challenge and stress levels. We then developed machine learning models to predict better stress intervention delivery timing based on this multi-modal data. We found that features from computer and application usage, activity, heart rate variability and stress intervention history showed up to 80.0% accuracy in predicting good or bad intervention timing using a multi-kernel support vector machine algorithm. These findings could help practitioners design the most effective, just-in-time, closed-loop, stress interventions. To our knowledge, this is one of the first papers to review opportune stress interventions' delivery timing research, which could have a big influence in designing stress intervention technologies.

Intelligent agents have the potential to help with many tasks. Information seeking and voice-enabled search assistants are becoming very common. However, there remain questions as to the extent by which these agents should sense and respond to emotional signals. We designed a set of information seeking tasks and recruited participants to complete them using a human intermediary. In total we collected data from 22 pairs of individuals, each completing five search tasks. The participants could communicate only using voice, over a VoIP service. Using automated methods we extracted facial action, voice prosody and linguistic features from the audio-visual recordings. We analyzed the characteristics of these interactions that correlated with successful communication and understanding between the pairs. We found that those who were expressive in channels that were missing from the communication channel (e.g., facial actions and gaze) were rated as communicating poorly, being less helpful and understanding. Having a way of reinstating nonverbal cues into these interactions would improve the experience, even when the tasks are purely information seeking exercises. The dataset used for this analysis contains over 15 hours of video, audio and transcripts and reported ratings. It is publicly available for researchers at: http://aka.ms/MISCv1.

Software-based agents are becoming increasingly ubiquitous and automated. However, current technology and algorithms are still fallible, which considerably affects users’ trust and interaction with such agents. In this article, we investigate two factors that can engender user trust in agents: reliability and attractiveness of agents. We show that agent reliability is not more important than agent attractiveness. Subjective user ratings of agent trust and perceived accuracy suggest that attractiveness may be even more important than reliability.

In HCI research, attention has focused on understanding external influences on workplace multitasking. We explore instead how multitasking might be influenced by individual factors: personality, stress, and sleep. Forty information workers' online activity was tracked over two work weeks. The median duration of online screen focus was 40 seconds. The personality trait of Neuroticism was associated with shorter online focus duration and Impulsivity-Urgency was associated with longer online focus duration. Stress and sleep duration showed trends to be inversely associated with online focus. Shorter focus duration was associated with lower assessed productivity at day's end. Factor analysis revealed a factor of lack of control which significantly predicts multitasking. Our results suggest that there could be a trait for distractibility where some individuals are susceptible to online attention shifting in the workplace. Our results have implications for information systems (e.g. educational systems, game design) where attention focus is key.

While email provides numerous benefits in the workplace, it is unclear how patterns of email use might affect key workplace indicators of productivity and stress. We investigate how three email use patterns: duration, interruption habit, and batching, relate to perceived workplace productivity and stress. We tracked email usage with computer logging, biosensors and daily surveys for 40 information workers in their in situ workplace environments for 12 workdays. We found that the longer daily time spent on email, the lower was perceived productivity and the higher the measured stress. People who primarily check email through self-interruptions report higher productivity with longer email duration compared to those who rely on notifications. Batching email is associated with higher rated productivity with longer email duration, but despite widespread claims, we found no evidence that batching email leads to lower stress. We discuss the implications of our results for improving organizational email practices.

Given the importance of creativity for both personal and societal achievements, there have been consistent efforts to stimulate creative ability. But an important environmental factor --- blue (i.e., short wavelength) light --- has been relatively unexplored to date. Blue light improves a number of cognitive processes (e.g., attention, working memory and sleep) known to influence our creative abilities. In this study, we investigate the effects of blue light on enhancing creativity in tasks and compare it to the effects of walking, which has been shown to stimulate creative ability. Based on data from 21 participants over 2 weeks, we found that blue light resulted in a 24.3% increase in convergent thinking ability, while walking improved divergent thinking by 18%. We discuss the implications of the findings within the context of UbiComp research. To the best of our knowledge, this is the first systematic examination of the impact of blue light on convergent and divergent thinking ability.

Despite a long history and a large volume of affective research, measuring affective states is still a non-trivial task that is complicated by numerous conceptual and methodological decisions that the researcher has to make. We suggest that inconsistent results reported in some areas of research can be partially explained by the choice of measurements that capture different manifestations of affective phenomena, or focus on different elements of affective processes. In the present study we examine one of such topics - a relationship between stress and individual's work role. In a 2-week, multi-method in situ study we collected affective information from 40 subjects. All participants provided continuous physiological (cardiovascular) data for the entire duration of the study, submitted multiple daily self-reports of momentary affect, and filled out a onetime assessment of the global perceived stress. We found that individuals' job role (specifically, decision-making workload) was not related to the cumulative measures of momentary affect, but was negatively correlated with the overall level of perceived stress. We further found that this negative relationship was partially mediated by individuals' coping behaviors. Our results emphasize the important difference between fleeting and global (appraised) affective states, and remind about intervening variables that can significantly modify affective processes. We suggest directions for future research and discuss practical applications for stress management.

Accurate and timely assessment of collective emotions in the workplace is a critical managerial task. However, perceptual, normative, and methodological challenges make it very difficult even for the most experienced organizational leaders. In this paper we present a MoodTracker - a technological solution that can help to overcome these challenges, and facilitate a continuous monitoring of the collective emotions in large groups in real-time. The MoodTracker is a program that runs on any PC device, and provides users with an interface for self-report of their affect. The device was tested in situ for four weeks, during which we received over 3000 emotion self-reports. Based on the usage data, we concluded that users had a positive attitude toward the MoodTracker and favorably evaluated its utility. From the collected data we were also able to establish some patterns of weekly and daily variations of employees' emotions in the workplace. We discuss practical applications and suggest directions for future development.

We developed a feedback-loop, user-tailored advice system to provide stress interventions and advice about improving sleep, diet, and exercise habits at the workplace. Thirty participants joined a 2 week study: in the first week, we collected their behaviors about sleep, diet, exercise and stress levels using Fitbit and surveys. During the second week we continued monitoring, and based on the participants' measurements in the previous days, we also provided interventions and advice during the workday, and evaluated their preferences. We found that participants with higher stress levels liked stress interventions more and that somatic activities were most preferred and reduced stress levels the most. We observed individual preference differences in the types of advice; however, tracking and receiving advice raised users' awareness of their stress, sleep, exercise, and dietary behaviors. We found that the largest positive impact was on our participants' dietary behaviors.

A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research.

We present "Lightwear", a series of garment-based, lightweight, light-emitting wearables designed to administer light therapy for on-the-go treatment of Seasonal Affective Disorder (SAD). Bright Light Therapy (BLT) has been used to treat SAD for more than 25 years. While light boxes continue to serve as the predominant method of treatment, it often requires a user to sit at a dedicated location for a sustained period of time (30-60 minutes), rendering therapy inconvenient and resulting in unsatisfactory compliance rates. To date, there have been few successful products developed for wearability and portability to ease the uncomfortable nature of light box treatment. However, new low-profile, light-emitting sources yield opportunities for less cumbersome textile integration and wearability. We explore the integration of light into textile substrates that focus on fashion-forward wearables which can, in turn, address BLT efficacy, usability, and convenience.

We present SWARM, a wearable affective technology designed to help a user to reflect on their own emotional state, modify their affect, and interpret the emotional states of others. SWARM aims for a universal design (inclusive of people with various disabilities), with a focus on modular actuation components to accommodate users' sensory capabilities and preferences, and a scarf form-factor meant to reduce the stigma of accessible technologies through a fashionable embodiment. Using an iterative, user-centered approach, we present SWARM's design. Additionally, we contribute findings for communicating emotions through technology actuations, wearable design techniques (including a modular soft circuit design technique that fuses conductive fabric with actuation components), and universal design considerations for wearable technology.

Recognizing when computer users are stressed can help reduce their frustration and prevent a large variety of negative health conditions associated with chronic stress. However, measuring stress non-invasively and continuously at work remains an open challenge. This work explores the possibility of using a pressure-sensitive keyboard and a capacitive mouse to discriminate between stressful and relaxed conditions in a laboratory study. During a 30 minute session, 24 participants performed several computerized tasks consisting of expressive writing, text transcription, and mouse clicking. During the stressful conditions, the large majority of the participants showed significantly increased typing pressure (>79% of the participants) and more contact with the surface of the mouse (75% of the participants). We discuss the potential implications of this work and provide recommendations for future work.

While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.

Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women's brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.

This paper presents novel human computation experiments geared towards uncovering the structure of affect. Using Mechanical Turk workers across 2 separate studies, we empirically verified some of the popular beliefs about the structure of affect, but also provide some new evidence. We replicate and reveal not only the statistical structure of the dimensions of affect, but also the effect of cultural influences. We close with a proposition for a framework for doing this kind of large scale research and provide recommendations and opportunities for innovations in research around emotional theory.

In his 1962 short story, "1000 Dreams of Bellavista," sci-fi author J. G Ballard describes a future in which "psychotropic" homes exist and are designed to "feel and react" to the emotions of their occupants [1]. Today with the rise of affective computing, and advancements in e-textiles, smart materials and sensor technologies, we must consider the ramifications of technology that could actively mirror, alter and transform our feelings through the materials that make up our buildings and environments. This work provides discussion and insight around the binding of material and sensor technologies with affect. We investigate how emotions could be mapped to our environment through textiles. We present two online surveys designed to enable people to map emotions to textiles. We then use the results of these surveys to inform and inspire the design of the Textile Mirror prototype, a 60x92 cm wall panel that is composed of industrial felt interlaced with Nitinol wire, and is designed to shift its textural structure upon receiving emotional signals from its viewer.

Visual analytics involves complex analytical processes that can often benefit from collaboration. Many researchers have explored co-located synchronous systems to help support collaborative visual analytics; however, the process can often be long and require a series of sessions. Providing support for asynchronous collaboration in visual analytics systems can help divide the problem between several analysts across many sessions to ensure that they can effectively work together toward a solution. Currently, visual analytics systems offer limited support for asynchronous, multi-session work [1]. In this workshop, we seek to bring together researchers from both the CSCW and Visual Analytics communities to discuss avenues for supporting asynchronous collaboration in visual analytics system.

We present AffectAura, an emotional prosthetic that allows users to reflect on their emotional states over long periods of time. We designed a multimodal sensor set-up for continuous logging of audio, visual, physiological and contextual data, a classification scheme for predicting user affective state and an interface for user reflection. The system continuously predicts a user's valence, arousal and engage-ment, and correlates this with information on events, communications and data interactions. We evaluate the interface through a user study consisting of six users and over 240 hours of data, and demonstrate the utility of such a reflection tool. We show that users could reason forward and backward in time about their emotional experiences using the interface, and found this useful.

Tools exist for people to create visualizations with their data; however, they are often designed for programmers or they restrict less technical people to pre-defined templates. This can make creating novel, custom visualizations difficult for the average person. For example, existing tools typically do not support syntax or interaction techniques that are natural to end users. To explore how to support a more natural production of data visualizations by end users, we conducted an exploratory study to illuminate the structure and content of the language employed by end users when describing data visualizations. We present our findings from the study and discuss their design implications for future visualization languages and toolkits.

Work teams are often geographically distributed, and in some cases, experience large time-zone differences with no overlap in working hours. We explored the use of asynchronous video in temporally distributed teams. We developed VideoThreads, which provides a novel thread-based visualization of video messages. Based on a deployment to four teams, we offer design recommendations and insights about the benefits of asynchronous video sharing.

Despite the pervasiveness of markets in our lives, little is known about the role of user interfaces (UIs) in promoting good decisions in market domains. How does the way we display market information to end users, and the set of choices we offer, influence users' decisions? In this paper, we introduce a new research agenda on "market user interface design." Our goal is to find the optimal market UI, taking into account that users incur cognitive costs and are boundedly rational. Via lab experiments we systematically explore the market UI design space, and we study the automatic optimization of market UIs given a behavioral (quantal response) model of user behavior. Surprisingly, we find that the behaviorally-optimized UI performs worse than the standard UI, suggesting that the quantal response model did not predict user behavior well. Subsequently, we identify important behavioral factors that are missing from the user model, including loss aversion and position effects, which motivates follow-up studies. Furthermore, we find significant differences between individual users in terms of rationality. This suggests future research on personalized UI designs, with interfaces that are tailored towards each individual user's needs, capabilities, and preferences.

Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams’ collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.

We explore the use of social learning - improving knowledge skills by observing peer behavior - in the domain of Web search skill acquisition, focusing specifically on co-located classroom scenarios. Through a series of interviews, pilot studies, and classroom deployments, we conclude that a peripheral display of Web search activity within a classroom facilitates both social learning and teacher-led discourse. We present the ClassSearch system for shared awareness of Web search activity, which embodies principles gleaned from our iterative design process, and show results from a ClassSearch deployment in twelve middle-school classroom sessions. Finally, we highlight design suggestions and opportunities for future work while taxonomizing the space of co-located search pedagogies.

We propose a novel system to analyze gestural and nonverbal cues of participants in video conferencing. These cues have previously been referred to as “honest signals” and are usually associated with the underlying cognitive state of the participants. The presented system analyzes a set of audio-visual, non-linguistic features in real time from the audio and video streams of two participants in a video conference. We show how these features can be used to compute indicators of the overall quality and type of conversation being held. The system also provides visual feedback to the participants, who then have the choice of modifying their conversational style in order to achieve the desired outcome of the video conference. Experiments on real-life data show that the system can predict the type of conversation with high accuracy using the non-linguistic signals only. Qualitative user studies highlight the positive effects of increased awareness amongst the participants about their own gestural and non-verbal cues.

Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e.g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks.

The Internet has allowed market-based systems to become increasingly pervasive. In this paper we explore the role of user interface (UI) design for these markets. Different UIs induce different mental models which in turn determine how users understand and interact with a market. Thus, the intersection of UI design and economics is a novel and important research area. We make three contributions at this intersection. First, we present a novel design paradigm which we call hidden markets. The primary goal of hidden markets is to hide as much of the market complexities as possible. Second, we explore this new design paradigm using one particular example: a P2P backup application. We explain the market underlying this system and provide a detailed description of the new UI we developed. Third, we present results from a formative usability study. Our findings indicate that a number of users could benefit from a market-based P2P backup system. Most users intuitively understood the give & take principle as well as the bundle constraints of the market. However, the pricing aspect was difficult to discover/understand for many users and thus needs further investigation. Overall, the results are encouraging and show promise for the hidden market paradigm.

Previous work has demonstrated the benefits of spatial audio conferencing over monophonic when listening to a group conversation. In this paper we examined three-way distributed conversations while varying the presence of spatial video and audio. Our results demonstrate significant benefits to adding spatialized video to an audio conference. Specifically, users perceived that the conversations were of higher quality, they were more engaged, and they were better able to keep track of the conversation. In contrast, no significant benefits were found when mono audio was replaced by spatialized audio. The results of this work are important in that they provide strong evidence for continued exploration of spatialized video, and also suggest that the benefits of spatialized audio may have less of an impact when video is also spatialized.

Although there has been significant research into gender regarding educational and workplace practices, there has been little investigation of gender differences pertaining to problem solving with programming tools and environments. As a result, there is little evidence as to what role gender plays in programming tools---and what little evidence there is has involved mainly novice and end-user programmers in academic studies. This paper therefore investigates how widespread such phenomena are in industrial programming situations, considering three disparate programming populations involving almost 3000 people and three different programming platforms in industry. To accomplish this, we analyzed four industry "legacy" studies from a gender perspective, triangulating results against each other and against a new fifth study, also in industry. We investigated gender differences in software feature usage and in tinkering/exploring software features. Furthermore, we examined how such differences tied to confidence. Our results showed significant gender differences in all three factors---across all populations and platforms.

When information is known only to friends in a social network, traditional crowdsourcing mechanisms struggle to motivate a large enough user population and to ensure accuracy of the collected information. We thus introduce friendsourcing, a form of crowdsourcing aimed at collecting accurate information available only to a small, socially-connected group of individuals. Our approach to friendsourcing is to design socially enjoyable interactions that produce the desired information as a side effect. We focus our analysis around Collabio, a novel social tagging game that we developed to encourage friends to tag one another within an online social network. Collabio encourages friends, family, and colleagues to generate useful information about each other. We describe the design space of incentives in social tagging games and evaluate our choices by a combination of usage log analysis and survey data. Data acquired via Collabio is typically accurate and augments tags that could have been found on Facebook or the Web. To complete the arc from data collection to application, we produce a trio of prototype applications to demonstrate how Collabio tags could be utilized: an aggregate tag cloud visualization, a personalized RSS feed, and a question and answer system. The social data powering these applications enables them to address needs previously difficult to support, such as question answering for topics comprehensible only to a few of a user's friends.

Previous research has shown that faceted browsing is effective and enjoyable in searching and browsing large collections of data. In this work, we explore the efficacy of interactive visualization systems in supporting exploration and sensemaking within faceted datasets. To do this, we developed an interactive visualization system called FacetLens, which exposes trends and relationships within faceted datasets. FacetLens implements linear facets to enable users not only to identify trends but also to easily compare several trends simultaneously. Furthermore, it offers pivot operations to allow users to navigate the faceted dataset using relationships between items. We evaluate the utility of the system through a description of insights gained while experts used the system to explore the CHI publication repository as well as a database of funding grant data, and report a formative user study that identified usability issues.

Media spaces and videoconference systems are beneficial for connecting separated co-workers and providing rich contextual information. However, image sharing communication tools may also touch on sensitive spots of the human psyche related to personal, perceived image issues (e.g., appearance, self-image, self-presentation and vanity). We conducted two user studies to examine the impact of self-image concerns on the use of media spaces and videoconference systems. Our results suggest that personal, perceived image concerns have a considerable impact on the comfort level of users and may hinder effective communication [8]. We also found that image filtering techniques can help users feel more comfortable. Our results revealed that distortion filters, which are frequently cited to help preserve privacy, do not tend to be the ones preferred by users. Instead, users seemed to favor filters that make subtle changes to their appearance, or, in some instances, they preferred to use a surrogate instead.

We present Collabio, a social tagging game within an online social network that encourages friends to tag one another. Collabio's approach of incentivizing members of the social network to generate information about each other produces personalizing information about its users. We report usage log analysis, survey data, and a rating exercise demonstrating that Collabio tags are accurate and augment information that could have been scraped online.

CitationBuxton and Myers (1986) compared a specialized document navigation device and a scrollbar. Their device tracked finger movements along two touch-sensitive areas: one for absolute movement and one for relative movement. They found that their system was faster when navigating a 5-page document. We identified finger-tracking issues with the Buxton and Myers device for larger documents (100 pages) and developed an alternative device employing a range sensor and rotary encoder to track finger movement. We ran a new experiment comparing the traditional scrollbar, the Buxton and Myers design, and our new design. Both the Buxton and Myers design and our new design were poorly received by users compared to the scrollbar. Our results indicate that in a large document, factors beyond finger tracking accuracy influence the performance of a device providing absolute movement. From these results, we identify possible improvements necessary to implement effective and practical absolute navigation devices.

We present a new interaction framework for collaborating in multiple display environments (MDEs) and report results from a field study investigating its use in an authentic work setting. Our interaction framework, IMPROMPTU, allows users to share task information across displays via off-the-shelf applications, to jointly interact with information for focused problem solving and to place information on shared displays for discussion and reflection. Our framework also includes a lightweight interface for performing these and related actions. A three week field study of our framework was conducted in the domain of face-to-face group software development. Results show that teams utilized almost every feature of the framework in support of a wide range of development-related activities. The framework was used most to facilitate opportunistic collaboration involving task information. Teams reported wanting to continue using the framework as they found value in it overall.

While proponents of adaptive user interfaces tout potential performance gains, critics argue that adaptation's unpredictability may disorient users, causing more harm than good. We present a study that examines the relative effects of predictability and accuracy on the usability of adaptive UIs. Our results show that increasing predictability and accuracy led to strongly improved satisfaction. Increasing accuracy also resulted in improved performance and higher utilization of the adaptive interface. Contrary to our expectations, improvement in accuracy had a stronger effect on performance, utilization and some satisfaction ratings than the improvement in predictability.

We present RelAltTab, an enhanced ALT+TAB prototype that assists users in switching windows. Our approach uses semantic and temporal information to create a list of related windows to the window that the user is currently engaged in. The main assumption is that the user is more likely to switch to a related window than to any other window in the system. We propose two different user interfaces that present the related window list to the user. We describe in detail the techniques and user interfaces of the RelAltTab system, and present the results of one user study comparing our approach to the standard Windows ALT+TAB program.

Software developers spend significant time gaining and maintaining awareness of fellow developers' activities. FASTDash is a new interactive visualization that seeks to improve team activity awareness using a spatial representation of the shared code base that highlights team members' current activities. With FASTDash, a developer can quickly determine which team members have source files checked out, which files are being viewed, and what methods and classes are currently being changed. The visualization can be annotated, allowing programmers to supplement activity information with additional status details. It provides immediate awareness of potential conflict situations, such as two programmers editing the same source file. FASTDash was developed through user-centered design, including surveys, team interviews, and in situ observation. Results from a field study show that FASTDash improved team awareness, reduced reliance on shared artifacts, and increased project-related communication. Additionally, the team that participated in our field study continues to use FASTDash.

Software can now track which computer applications and documents you use. This provides us with the potential to help end-users recall past activities for tasks such as status reporting. We describe findings from field observations of eight participants writing their status reports. We observed interesting trends, including the reliance on memory triggers, which were either retrieved from explicit self-reminders, from implicit breadcrumbs left while performing their tasks or directly from memory. Participants perceived spending relatively short amounts of time composing their status reports, suggesting that any technology solution must offer dramatic improvements over current practice.

Currently there are no dialog systems that enable purely voice-based access to the unstructured information on websites such as Wikipedia. Such systems could be revolutionary for non-literate users in the developing world. To investigate interface issues in such a system, we developed VoicePedia, a telephone-based dialog system for searching and browsing Wikipedia. In this paper, we present the system, as well as a user study comparing the use of VoicePedia to SmartPedia, a Smartphone GUI-based alternative. Keyword entry through the voice interface was significantly faster, while search result navigation, and page browsing were significantly slower. Although users preferred the GUI-based interface, task success rates between both systems were comparable – a promising result for regions where Smartphones and data plans are not viable. Index Terms: dialog system, information access

For decades, researchers have presented different adaptive user interfaces and discussed the pros and cons of adaptation on task performance and satisfaction. Little research, however, has been directed at isolating and understanding those aspects of adaptive interfaces which make some of them successful and others not. We have designed and implemented three adaptive graphical interfaces and evaluated them in two experiments along with a non-adaptive baseline. In this paper we synthesize our results with previous work and discuss how different design choices and interactions affect the success of adaptive graphical user interfaces.

Accessing and manipulating occluded content in layered 2D drawings can be difficult. This paper characterizes a design space of techniques that facilitate access to occluded content. In addition, we introduce two new tools, Tumbler and Splatter, which represent unexplored areas of the design space. Finally, we present results of a study that contrasts these two tools against the traditional scene index used in most drawing applications. Results show that Splatter is comparable to and can be better than the scene index. Our findings allow us to understand the inherent design tradeoffs, and to identify areas for further improvement.

In this paper we describe a novel approach for searching large data sets from a mobile phone. Existing interfaces for mobile search require keyword text entry and are not suited for browsing. Our alternative uses a hybrid model to de-emphasize tedious keyword entry in favor of iterative data filtering. We propose navigation and selection of hierarchical metadata (facet navigation), with incremental text entry to further narrow the results. We conducted a formative evaluation to understand the relative advantages of keyword entry versus facet navigation for both browse and search tasks on the phone. We found keyword entry to be more powerful when the name of the search target is known, while facet navigation is otherwise more effective and strongly preferred.

Information workers often have to balance many tasks and interruptions. In this work, we explore peripheral display techniques that improve multitasking efficiency by helping users maintain task flow, know when to resume tasks, and more easily reacquire tasks. Specifically, we compare two types of abstraction that provide different task information: semantic content extraction, which displays only the most relevant content in a window, and change detection, which signals when a change has occurred in a window (all de-signed as modifications to Scalable Fabric [17]). Results from our user study suggest that semantic content extraction improves multitasking performance more so than either change detection or our base case of scaling. Results also show that semantic content extraction provides significant benefits to task flow, resumption timing, and reacquisition. We discuss the implication of these findings on the design of peripheral interfaces that support multitasking.

Modern development environments provide many features for navigating source code, yet studies show the developers still spend a tremendous amount of time just navigating. Since existing navigation features rely heavily on memorizing symbol names, we present a new design, called code thumbnails, intended to allow a developer to navigate source code by forming a spatial memory of it. To aid intra-file navigation, we add a thumbnail image of the file to the scrollbar, which makes any part of the file one click away. To aid interfile navigation, we provide a desktop of file thumbnail images, which make any part of any file one click away. We did a formative evaluation of the design with eleven experienced developers and present the results

Creativity support tools is a research topic with high risk but potentially very high payoff. The goal is to develop improved software and user interfaces that empower users to be not only more productive but also more innovative. Potential users include software and other engineers, diverse scientists, product and graphic designers, architects, educators, students, and many others. Enhanced interfaces could enable more effective searching of intellectual resources, improved collaboration among teams, and more rapid discovery processes. These advanced interfaces should also provide potent support in hypothesis formation, speedier evaluation of alternatives, improved understanding through visualization, and better dissemination of results. For creative endeavors that require composition of novel artifacts (e.g., computer programs, scientific papers, engineering diagrams, symphonies, artwork), enhanced interfaces could facilitate exploration of alternatives, prevent unproductive choices, and enable easy backtracking. This U.S. National Science Foundation sponsored workshop brought together 25 research leaders and graduate students to share experiences, identify opportunities, and formulate research challenges. Two key outcomes emerged: (a) encouragement to evaluate creativity support tools through multidimensional in-depth longitudinal case studies and (b) formulation of 12 principles for design of creativity support tools.

The dominant paradigm for searching and browsing large data stores is text-based: presenting a scrollable list of search results in response to textual search term input. While this works well for the Web, there is opportunity for improvement in the domain of personal information stores, which tend to have more heterogeneous data and richer metadata. In this paper, we introduce FacetMap, an interactive, query-driven visualization, generalizable to a wide range of metadata-rich data stores. FacetMap uses a visual metaphor for both input (selection of metadata facets as filters) and output. Results of a user study provide insight into tradeoffs between FacetMap's graphical approach and the traditional text-oriented approach.

HIPs, or Human Interactive Proofs, are challenges meant to be easily solved by humans, while remaining too hard to be economically solved by computers. HIPs are increasingly used to protect services against automatic script attacks. To be effective, a HIP must be difficult enough to discourage script attacks by raising the computation and/or development cost of breaking the HIP to an unprofitable level. At the same time, the HIP must be easy enough to solve in order to not discourage humans from using the service. Early HIP designs have successfully met these criteria [1]. However, the growing sophistication of attackers and correspondingly increasing profit incentives have rendered most of the currently deployed HIPs vulnerable to attack [2,7,12]. Yet, most companies have been reluctant to increase the difficulty of their HIPs for fear of making them too complex or unappealing to humans. The purpose of this study is to find the visual distortions that are most effective at foiling computer attacks without hindering humans. The contribution of this research is that we discovered that 1) automatically generating HIPs by varying particular distortion parameters renders HIPs that are too easy for computer hackers to break, yet humans still have difficulty recognizing them, and 2) it is possible to build segmentation-based HIPs that are extremely difficult and expensive for computers to solve, while remaining relatively easy for humans.

In this paper we describe a novel approach to the visualization of the mapping between two schemas. Current approaches to visually defining such a mapping fail when the schemas or maps become large. The new approach uses various information visualization techniques to simplify the view, making it possible for users to effectively deal with much larger schemas and maps. A user study verifies that the new approach is useful, usable, and effective. The primary contribution is a demonstration of novel ways to effectively present highly complex information.

Large software projects often require a programmer to make changes to unfamiliar source code. This paper describes a set of tools, called Team Tracks, designed to ease program comprehension by showing the source code navigation patterns of fellow development team members. One technique shows a list of related items, given that the user is viewing a given method or class. Another technique shows the favorite classes, by showing a class hierarchy view that hides less frequently visited classes, methods, and members. Two user studies, a laboratory study and a field study, were run to evaluate the effectiveness of these techniques. The results of the two studies demonstrate that sharing navigation data can improve program comprehension and is subjectively preferred by users.

The continuing trend toward greater processing power, larger storage, and in particular increased display surface by using multiple monitor supports increased multi-tasking by the computer user. The concomitant increase in desktop complexity has the potential to push the overhead of window management to frustrating and counterproductive new levels. It is difficult to adequately design for multiple monitor systems without understanding how multiple monitor users differ from, or are similar to, single monitor users. Therefore, we deployed a tool to a group of single monitor and multiple monitor users to log window management activity. Analysis of the data collected from this tool revealed that usage of interaction components may change with an increase in number of monitors, and window visibility can be a useful measure of user display space management activity, especially for multiple monitor users. The results from this analysis begin to fill a gap in research about real-world window management practices.

Our studies have shown that as displays become larger, users leave more windows open for easy multitasking. A larger number of windows, however, may increase the time that users spend arranging and switching between tasks. We present Scalable Fabric, a task management system designed to address problems with the proliferation of open windows on the PC desktop. Scalable Fabric couples window management with a flexible visual representation to provide a focus-plus-context solution to desktop complexity. Users interact with windows in a central focus region of the display in a normal manner, but when a user moves a window into the periphery, it shrinks in size, getting smaller as it nears the edge of the display. The window "minimize" action is redefined to return the window to its preferred location in the periphery, allowing windows to remain visible when not in use. Windows in the periphery may be grouped together into named tasks, and task switching is accomplished with a single mouse click. The spatial arrangement of tasks leverages human spatial memory to make task switching easier. We review the evolution of Scalable Fabric over three design iterations, including discussion of results from two user studies that were performed to compare the experience with Scalable Fabric to that of the Microsoft Windows XP TaskBar.

We report on a diary study of the activities of information workers aimed at characterizing how people interleave multiple tasks amidst interruptions. The week-long study revealed the type and complexity of activities performed, the nature of the interruptions experienced, and the difficulty of shifting among numerous tasks. We present key findings from the diary study and discuss implications of the findings. Finally, we describe promising directions in the design of software tools for task management, motivated by the findings. Author Keywords Multitasking, diary study, task switching, interruptions, information worker, office and workplace. ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI).

How do we break away from existing tools and techniques in HCI and truly innovate in a way that benefits the next generation of computer users? Today, too many of our technological designs and inventions are "one off" point designs, not building on or contributing to a theoretical foundation of understanding around human perception, cognition, social behavior and physical movement. Of course, these point designs can be successful in and of themselves, so why bother with theory and models? In order to mature as a field in a way that benefits users, it can be argued that we need to work more closely together and with an awareness of multiple disciplines, including not just the computer science and engineering arenas, but also psychology, sociology, and any field of human behavior. Of course, this could be a daunting task-how do we know that important improvements in user interface design can be obtained?

Calendar applications for small handheld devices are growing in popularity. This led us to develop DateLens, a novel calendar interface for PDAs designed to support complex tasks. It uses a fisheye representation coupled with compact overviews to give the big picture in a small space. The interface also gives users control over the visible time period, as well as supporting integrated search to discover patterns and outliers. Designed with device scalability in mind, DateLens currently runs on desktop computers as well as PDAs. Two user studies were conducted to examine the viability of DateLens as a replacement for traditional calendar visualizations. In the first study, non-PDA users performed complex tasks significantly faster with DateLens than with the Microsoft Pocket PC 2002 TM calendar (using a PDA emulator). In addition, they rated DateLens as being easier to use than the default calendar application for a majority of the tasks. In the second study, the participants were expert Pocket PC users and the software was run on their own devices. Again, DateLens performed significantly faster for the complex tasks, and there were satisfaction differences favoring each calendar for different kinds of tasks. From these studies, it is clear that DateLens is superior for more complex tasks such as those associated with longer time periods. For daily event tracking, users familiar with the default Pocket PC calendar strongly preferred its daily view and behaviors.

Previous research reported interesting gender effects involving specific benefits for females navigating with wider fields of view on large displays. However, it was not clear what was driving the 3D navigation performance gains, and whether or not the effect was more tightly coupled to gender or to spatial abilities. The study we report in this paper replicates and extends previous work, demonstrating that the gender-specific navigation benefits come from the presence of optical flow cues, which are better afforded by wider fields of view on large displays. The study also indicates that the effect may indeed be tied to gender, as opposed to spatial abilities. Together, the findings provide a significant contribution to the HCI community, as we provide strong recommendations for the design and presentation of 3D environments, backed by empirical data. Additionally, these recommendations reliably benefit females, without an accompanying detriment to male navigation performance.

We describe the design and functionality of the Scope, a glanceable notification summarizer. The Scope is an information visualization designed to unify notifications and minimize distractions. It allows users to remain aware of notifications from multiple sources of information, including e-mail, instant messaging, information alerts, and appointments. The design employs a circular radar-like screen divided into sectors that group different kinds of notifications. The more urgent a notification is, the more centrally it is placed. Visual emphasis and annotation is used to reveal important properties of notifications. Several natural gestures allow users to zoom in on particular regions and to selectively drill down on items. We present key aspects of the Scope design, review the results of an initial user study, and describe the motivation and outcome of an iteration on the visual design.

Published reports suggest that males significantly outperform females in navigating virtual environments. A novel navigation technique reported in CHI 2001, when combined with a large display and wide field of view, appeared to reduce that gender bias. That work has been extended with two navigation studies in order to understand the finding under carefully controlled conditions. The first study replicated the finding that a wide field of view coupled with a large display benefits both male and female users and reduces gender bias. The second study suggested that wide fields of view on a large display were useful to females despite a more densely populated virtual world. Implications for design of virtual worlds and large displays are discussed. Specifically, women take a wider field of view to achieve similar virtual environment navigation performance to men

We describe a new information structure composed of multiple intersecting hierarchies, which we call Polyarchies. Visualizing polyarchies enables use of novel views for discovery of relationships which are very difficult using existing hierarchy visualization tools. This paper will describe the visualization design and system architecture challenges as well as our current solutions. A Mid-Tier Cache architecture is used as a "polyarchy server" which supports a novel web-based polyarchy visualization technique, called Visual Pivot. A series of five user studies guided iterative design of Visual Pivot

We present a task-based taxonomy of navigation techniques for 3D virtual environments, used to categorize existing techniques, drive exploration of the design space, and inspire new techniques. We briefly discuss several new techniques, and describe in detail one new techniques, Speed-coupled Flying with Orbiting. This technique couples control of movement speed to camera height and tilt, allowing users to seamlessly transition between local environment-views and global overviews. Users can also orbit specific objects for inspection. Results from two competitive user studies suggest users performed better with Speed-coupled Flying with Orbiting over alternatives, with performance also enhanced by a large display.

The Task Gallery is a window manager that uses interactive 3D graphics to provide direct support for task management and document comparison, lacking from many systems implementing the desktop metaphor. User tasks appear as artwork hung on the walls of a virtual art gallery, with the selected task on a stage. Multiple documents can be selected and displayed side-by-side using 3D space to provide uniform and intuitive scaling. The Task Gallery hosts any Windows application, using a novel redirection mechanism that routes input and output between the 3D environment and unmodified 2D Windows applications. User studies suggest that the Task Gallery helps with task management, is enjoyable to use, and that the 3D metaphor evokes spatial memory and cognition.

The practical significance of identifying and accommodating individual differences has been established across a number of fields of research. There is a renewed interest in individual differences due to the advances in virtual environments, especially through far-reaching technologies such as information visualization and 3D graphical user interfaces on the World Wide Web. The effects of individual differences on the use of these new technologies are yet to be found out. More fundamentally, theories and methods developed for the earlier generations of information systems are subject to a close examination of their applicability, efficiency, and effectiveness. In this article, we present a brief historical overview of research in individual differences in the context of virtual environments. In particular, we highlight the notion of structure in the perception of individual users of an information system and the role of individuals' abilities to recognize and use such structures to perform various information-intensive tasks. Striking the balance between individuals' abilities and the demanding task for detecting, understanding, and utilizing such structures is an emerging theme across the five articles in this special issue. We outline the approaches and the major findings of these articles with reference to this central theme.

In this paper, we describe the use of similarity metrics in a novel visual environment for storing and retrieving favorite web pages. The similarity metrics, called Implicit Queries, are used to automatically highlight stored web pages that are related to the currently selected web page. Two experiments explored how users manage their personal web information space with and without the Implicit Query highlighting and later retrieve their stored web pages. When storing and organizing web pages, users with Implicit Query highlighting generated slightly more categories. Implicit Queries also led to faster web page retrieval time, although the results were not statistically significant.

ABSTNACTMuch is known about depth and breadth tradeoff issues in graphical user interface menu design.We describe an experiment to see if large breadth and decreased depth is preferable, both subjectively and via performance data, while attempting to design for optimal scent throughout different structures of a website.A study is reported which modified previous procedures for investigating depth/breadth tradeoffs in content design for the web.Results showed that, while increased depth did harm search performance on the web, a medium condition of depth and breadth outperformed the broadesf shallow web structure overall.

The number of people exploring the World Wrde Web is growing dramatically.Many companies are interested in the potential of advertising on the web, but there is little research to guide their decision.The present study demonstrates the concept of a corporate sponsored website as a promising direction for web advertisers.Advertisements for products targetiug lo-14 year-o& were presented as web games and inserted into a prototype website.For example, in one ad, players scored points by actively steering toward the advertised product with their web video-game car, while dodging undesirable obstacles on the road (sound effects included).For comparison, subjects also watched a TV ad for the same product embedded in a TV program.One day later, tests showed that web ads positively influenced how easily the advertised brand came to mind compared to TV ads and compared to no-ad controls.The effectiveness of interactive web ads for influencing consumers' memory in this preliiinaty study suggests that it merits more attention as a potentially viable medium for advertismg.Factors that could contribute to the advantage for web ads are ,discussed.

We describe input devices and two-handed interaction techniques to support map navigation tasks. We discuss several design variations and user testing of two-handed navigation techniques, including puck and stylus input on a Wacom tablet, as well as a novel design incorporating a touchpad (for the nonpreferred hand) and a mouse (for the preferred hand). To support the latter technique, we introduce a new input device, the TouchMouse, which is a standard mouse augmented with a pair of one-bit touch sensors, one for the palm and one for the index finger. Finally, we propose several enhancements to Buxton's three-state model of graphical input and extend this model to encompass two-handed input transactions as well. Keywords Two-handed input, three-state model, input devices, tablets, touchpads, TouchMouse, map navigation INTRODUCTION Two-handed input is a promising technique to improve the directness and degree of manipulation afforded by desktop computers. A strong foundation of re...

Effective management of documents on computers has been a central user interface problem for many years.One common approach involves using 2D spatial layouts of icons representing the documents, particularly for information workspace tasks.This approach takes advantage of human 2D spatial cognition.More recently, several 3D spatial layouts have engaged 3D spatial cognition capabilities.Some have attempted to use spatial memory in 3D virtual environments.However, there has been no proof to date that spatial memory works the same way in 3D virtual environments as it does in the real world.We describe a new technique for document management called the Data Mountain, which allows users to place documents at arbitrary positions on an inclined plane in a 3D desktop virtual environment using a simple 2D interaction technique.We discuss how the design evolved in response to user feedback.We also describe a user study that shows that the Data Mountain does take advantage of spatial memory.Our study shows that the Data Mountain has statistically reliable advantages over the Microsoft Internet Explorer Favorites mechanism for managing documents of interest in an information workspace.

This paper explores techniques for evaluating and improving immersion in Desktop Virtual Reality (VR). Three experiments are reported which extend findings on immersion in VR reported by Pausch et al. [9]. In the current experiments, a visual search paradigm was used to examine navigation in Desktop VR both with and without navigational aids. Pausch et al. found that non-head tracked users took significantly longer than predicted when the search target was absent, which was interpreted as indicative of a loss of sense of immersion. Our first experiment extended the Pausch et al. experiment to a desktop display. Our findings differ in that search times matched prediction when the target was absent, indicating that the Pausch et al. study does not transfer to Desktop VR. In the second and third experiments, our visual search task was performed while navigating a set of 3D hallways. We introduce a new navigation aid called Peripheral L.enses, intended to provide simulated peripheral vision. Informal studies suggested that Peripheral Lenses decrease search time, indicating an enhanced sense of immersion in Desktop VR. However, formal studies contradict that, demonstrating the importance of formal usability studies in the development of user interface software. We also gained evidence that visual attention findings transfer to Desktop VR.