DevLan/vispubs huggingface dataset:
Designing Information for Remediating Cognitive Biases in Decision-Making
Authors: Yunfeng Zhang, Rachel K. E. Bellamy, Wendy A. Kellogg
Published at CHI, 2015

Software is playing an increasingly important role in supporting human decision-making. Previous HCI research on decision support systems (DSS) has improved the information visualization aspect of DSS information design, but has somewhat overlooked the cognitive aspect of decision-making, namely that human reasoning is heuristic and reflects systematic errors or cognitive biases. We report on an empirical study of two cognitive biases: conservatism and loss aversion. Two remediation techniques recommended by previous research were tested: the expected return method, an actuarial-inspired approach presenting objective metrics; and bootstrapping, a technique successful in improving judgment consistency. The results show that the two biases can occur simultaneously and can have a huge impact on decision-making. The results also show that the two debiasing techniques are only partly effective. These findings suggest a need for more research on debiasing, and indicate some directions for exploring debiasing techniques and building decision support systems.
DocuViz: Visualizing Collaborative Writing
Authors: Dakuo Wang, Judith S. Olson, Jingwen Zhang, Trung Nguyen, Gary M. Olson
Published at CHI, 2015

Collaborative writing is on the increase. In order to write well together, authors often need to be aware of who has done what recently. We offer a new tool, DocuViz, that displays the entire revision history of Google Docs, showing more than the one-step-at-a-time view now shown in revision history and tracking changes in Word. We introduce the tool and present cases in which the tool has the potential to be useful: To authors themselves to see recent "seismic activity," indicating where in particular a co-author might want to pay attention, to instructors to see who has contributed what and which changes were made to comments from them, and to researchers interested in the new patterns of collaboration made possible by simultaneous editing capabilities.
Effects of Display Size and Resolution on User Behavior and Insight Acquisition in Visual Exploration
Authors: Khairi Reda, Andrew E. Johnson 0001, Michael E. Papka, Jason Leigh
Published at CHI, 2015

Large high-resolution displays are becoming increasingly common in research settings, providing data scientists with visual interfaces for the analysis of large datasets. Numerous studies have demonstrated unique perceptual and cognitive benefits afforded by these displays in visual analytics and information visualization tasks. However, the effects of these displays on knowledge discovery in exploratory visual analysis are still poorly understood. We present the results of a small-scale study to better understand how display size and resolution affect insight. Analyzing participants' verbal statements, we find preliminary evidence that larger displays with more pixels can significantly increase the number of discoveries reported during visual exploration, while yielding broader, more integrative insights. Furthermore, we find important differences in how participants performed the same visual exploration task using displays of varying sizes. We tie these results to extant work and propose explanations by considering the cognitive and interaction costs associated with visual exploration.
EnviroPulse: Providing Feedback about the Expected Affective Valence of the Environment
Authors: Deltcho Valtchanov, Mark S. Hancock
Published at CHI, 2015

Interacting with nature is beneficial to a person's mental-state, but it can sometimes be difficult to find environments that will induce positive affect (e.g., when planning a run). In this paper, we describe EnviroPulse-a system for auto-matically determining and communicating the expected affective valence (EAV) of environments to individuals. We describe a prototype that allows this to be used in real-time on a smartphone, but EnviroPulse could easily be incorporated into GPS systems, mapping services, or image-based systems. Our work differs from existing work in af-fective computing in that, rather than detecting a user's affect directly, we automatically determine the EAV of the environment through visual analysis. We present results that suggest our system can determine the EAV of envi-ronments. We also introduce real-time affective visual feedback of the calculated EAV of the images, and present results from an informal study suggesting that real-time visual feedback can be used for induction of affect.
Evaluating How Level of Detail of Visual History Affects Process Memory
Authors: Eric D. Ragan, John R. Goodall, Albert Tung
Published at CHI, 2015

Visual history tools provide visual representations of the workflow during data analysis tasks. While there is an established need for reviewing analytic processes, and many visual history tools provide visualizations to do so, it is not well known how helpful the tools actually are for process recall. Through a controlled experiment, we evaluated how the presence of a visual history aid and varying levels of visual detail affect process memory. Participants conducted an analysis task using a visual text-document analysis tool. We evaluated their memories of the process both immediately after the analysis and then again one week later. Results showed that even visual history views with reduced data-resolution were effective for aiding process memory. Further, even without inclusion of any data in the visual history aids, the visual cues alone from the final workspace were enough to improve memory of the main themes of analyses.
Evaluating the Memorability of Physical Visualizations
Authors: Simon Stusak, Jeannette Schwarz, Andreas Butz
Published at CHI, 2015

Physical Visualizations are currently mostly used in casual contexts, e.g., as artistic data sculptures. However, their measurable benefits for traditional information visualization are largely unexplored. As a step in this direction, we compared the memorability of physical visualizations to that of digital visualizations. We conducted a user study with 40 participants in which we measured the recall of three types of information immediately after exploration and with a delay of two weeks. The results show that the physical visualization led to significantly less information decay within this time span. Our results build on known effects from cognitive psychology and provide a first indicator for measurable benefits of physical visualizations regarding memorability.
Exploring Interactions with Physically Dynamic Bar Charts
Authors: Faisal Taher, John Hardy, Abhijit Karnik, Christian Weichel, Yvonne Jansen, Kasper Hornbæk, Jason Alexander
Published at CHI, 2015

Visualizations such as bar charts help users reason about data, but are mostly screen-based, rarely physical, and almost never physical and dynamic. This paper investigates the role of physically dynamic bar charts and evaluates new interactions for exploring and working with datasets rendered in dynamic physical form. To facilitate our exploration we constructed a 10x10 interactive bar chart and designed interactions that supported fundamental visualisation tasks, specifically; annotation, filtering, organization, and navigation. The interactions were evaluated in a user study with 17 participants. Our findings identify the preferred methods of working with the data for each task i.e. directly tapping rows to hide bars, highlight the strengths and limitations of working with physical data, and discuss the challenges of integrating the proposed interactions together into a larger data exploration system. In general, physical interactions were intuitive, informative, and enjoyable, paving the way for new explorations in physical data visualizations.
Exploring the Role of Activity Trace Design on Evaluations of Online Worker Quality
Authors: Jennifer Marlow, Laura A. Dabbish, Jodi L. Forlizzi
Published at CHI, 2015

Websites can record individual users' activities and display them in a variety of ways. There is a tradeoff between detail and abstraction in visualization, especially when the amount of content increases and becomes more difficult to process. We conducted an experiment on Mechanical Turk varying the quality, detail, and visual presentation of information about an individual's past work to see how these design features affected perceptions of the worker. We found that providing detail in the display through text increased processing time and led to less positive evaluations. Visually abstract displays required less processing time but decreased confidence in evaluation. This suggests that different design parameters may engender differing psychological processes that influence reactions towards an unknown person.
g-Miner: Interactive Visual Group Mining on Multivariate Graphs
Authors: Nan Cao 0001, Yu-Ru Lin, Liangyue Li, Hanghang Tong
Published at CHI, 2015

With the rapid growth of rich network data available through various sources such as social media and digital archives,there is a growing interest in more powerful network visual analysis tools and methods. The rich information about the network nodes and links can be represented as multivariate graphs, in which the nodes are accompanied with attributes to represent the properties of individual nodes. An important task often encountered in multivariate network analysis is to uncover link structure with groups, e.g., to understand why a person fits a specific job or certain role in a social group well.The task usually involves complex considerations including specific requirement of node attributes and link structure, and hence a fully automatic solution is typically not satisfactory.In this work, we identify the design challenges for min-ing groups with complex criteria and present an interactive system, "g-Miner," that enables visual mining of groups on multivariate graph data. We demonstrate the effectiveness of our system through case study and in-depth expert inter-views. This work contributes to understanding the design of systems for leveraging users' knowledge progressively with algorithmic capacity for tackling massive heterogeneous information.
Head-Mounted Display Visualizations to Support Sound Awareness for the Deaf and Hard of Hearing
Authors: Dhruv Jain, Leah Findlater, Jamie Gilkeson, Benjamin Holland, Ramani Duraiswami, Dmitry N. Zotkin, Christian Vogler, Jon E. Froehlich
Published at CHI, 2015

Persons with hearing loss use visual signals such as gestures and lip movement to interpret speech. While hearing aids and cochlear implants can improve sound recognition, they generally do not help the wearer localize sound necessary to leverage these visual cues. In this paper, we design and evaluate visualizations for spatially locating sound on a head-mounted display (HMD). To investigate this design space, we developed eight high-level visual sound feedback dimensions. For each dimension, we created 3-12 example visualizations and evaluated these as a design probe with 24 deaf and hard of hearing participants (Study 1). We then implemented a real-time proof-of-concept HMD prototype and solicited feedback from 4 new participants (Study 2). Study 1 findings reaffirm past work on challenges faced by persons with hearing loss in group conversations, provide support for the general idea of sound awareness visualizations on HMDs, and reveal preferences for specific design options. Although preliminary, Study 2 further contextualizes the design probe and uncovers directions for future work.
How Deceptive are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques
Authors: Anshul Vikram Pandey, Katharina Rall, Margaret L. Satterthwaite, Oded Nov, Enrico Bertini
Published at CHI, 2015

In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants' responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.
ImmerseBoard: Immersive Telepresence Experience using a Digital Whiteboard
Authors: Keita Higuchi, Yinpeng Chen, Philip A. Chou, Zhengyou Zhang, Zicheng Liu 0001
Published at CHI, 2015

ImmerseBoard is a system for remote collaboration through a digital whiteboard that gives participants a 3D immersive experience, enabled only by an RGBD camera (Microsoft Kinect) mounted on the side of a large touch display. Using 3D processing of the depth images, life-sized rendering, and novel visualizations, ImmerseBoard emulates writing side-by-side on a physical whiteboard, or alternatively on a mirror. User studies involving three tasks show that compared to standard video conferencing with a digital whiteboard, ImmerseBoard provides participants with a quantitatively better ability to estimate their remote partners' eye gaze direction, gesture direction, intention, and level of agreement. Moreover, these quantitative capabilities translate qualitatively into a heightened sense of being together and a more enjoyable experience. ImmerseBoard's form factor is suitable for practical and easy installation in homes and offices.
Infographic Aesthetics: Designing for the First Impression
Authors: Lane Harrison, Katharina Reinecke, Remco Chang
Published at CHI, 2015

Information graphics, or infographics, combine elements of data visualization with design and have become an increasingly popular means for disseminating data. While several studies have suggested that aesthetics in visualization and infographics relate to desirable outcomes like engagement and memorability, it remains unknown how quickly aesthetic impressions are formed, and what it is that makes an infographic appealing. We address these questions by analyzing 1,278 participants' ratings on appeal after seeing infographics for 500ms. Our results establish that: 1) people form a reliable first impression of the appeal of an infographic based on a mere exposure effect, 2) this first impression is largely based on colorfulness and visual complexity, and 3) age, gender, and education level influence the preferred level of colorfulness and complexity. More generally, these findings suggest that outcomes such as engagement and memorability might be determined much earlier than previously thought.
ISOTYPE Visualization: Working Memory, Performance, and Engagement with Pictographs
Authors: Steve Haroz, Robert Kosara, Steven L. Franconeri
Published at CHI, 2015

Although the infographic and design communities have used simple pictographic representations for decades, it is still unclear whether they can make visualizations more effective. Using simple charts, we tested how pictographic representations impact (1) memory for information just viewed, as well as under the load of additional information, (2) speed of finding information, and (3) engagement and preference in seeking out these visualizations. We find that superfluous images can distract. But we find no user costs -- and some intriguing benefits -- when pictographs are used to represent the data.
LeviPath: Modular Acoustic Levitation for 3D Path Visualisations
Authors: Themis Omirou, Asier Marzo, Sue Ann Seah, Sriram Subramanian
Published at CHI, 2015

LeviPath is a modular system to levitate objects across 3D paths. It consists of two opposed arrays of transducers that create a standing wave capable of suspending objects in mid-air. To control the standing wave, the system employs a novel algorithm based on combining basic patterns of movement. Our approach allows the control of multiple beads simultaneously along different 3D paths. Due to the patterns and the use of only two opposed arrays, the system is modular and can scale its interaction space by joining several LeviPaths. In this paper, we describe the hardware architecture, the basic patterns of movement and how to combine them to produce 3D path visualisations.
Making Software Tutorial Video Responsive
Authors: Cuong Nguyen 0003, Feng Liu 0015
Published at CHI, 2015

Tutorial videos are widely available to help people use software. These videos, however, are viewed by users as captured and offer little direct interaction between users and software. This paper presents a video navigation method that allows users to interact with software tutorial video as if they were using the software. To make the tutorial video responsive, our method records the user interaction events like mouse click and drag during capturing the video. Our method then analyzes, selects, and visualizes these user interaction events at the event locations. When a user directly interacts with an event visualization, our method automatically navigates to the proper video frame to provide the visual feedback as if the software were responding to the user input. Thus, our method provides the experience of interacting with the software through directly manipulating the tutorial video. Our study shows our method can better help users follow tutorial videos to complete tasks than the baseline timeline interface.
MatrixWave: Visual Comparison of Event Sequence Data
Authors: Jian Zhao 0010, Zhicheng Liu 0001, Mira Dontcheva, Aaron Hertzmann, Alan Wilson 0004
Published at CHI, 2015

Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram.
ModelTracker: Redesigning Performance Analysis Tools for Machine Learning
Authors: Saleema Amershi, Max Chickering, Steven Mark Drucker, Bongshin Lee, Patrice Y. Simard, Jina Suh
Published at CHI, 2015

Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous traditional summary statistics and graphs while displaying example-level performance and enabling direct error examination and debugging. Usage analysis from machine learning practitioners building real models with ModelTracker over six months shows ModelTracker is used often and throughout model building. A controlled experiment focusing on ModelTracker's debugging capabilities shows participants prefer ModelTracker over traditional tools without a loss in model performance.
Opportunities and Challenges for Data Physicalization
Authors: Yvonne Jansen, Pierre Dragicevic, Petra Isenberg, Jason Alexander, Abhijit Karnik, Johan Kildal, Sriram Subramanian, Kasper Hornbæk
Published at CHI, 2015

Physical representations of data have existed for thousands of years. Yet it is now that advances in digital fabrication, actuated tangible interfaces, and shape-changing displays are spurring an emerging area of research that we call Data Physicalization. It aims to help people explore, understand, and communicate data using computer-supported physical data representations. We call these representations physicalizations, analogously to visualizations -- their purely visual counterpart. In this article, we go beyond the focused research questions addressed so far by delineating the research area, synthesizing its open challenges and laying out a research agenda.
Patina Engraver: Visualizing Activity Logs as Patina in Fashionable Trackers
Authors: Moon-Hwan Lee, Seijin Cha, Tek-Jin Nam
Published at CHI, 2015

Despite technological improvements in commercial activity trackers, little attention has been given to their emotional, social, or fashion-related qualities, such as their visual aesthetics and their relationship to self-expression and social connection. As an alternative integrated approach incorporating HCI, fashion, and product design, our project made use of the characteristics of patina to improve activity trackers as fashionable wearables. We developed the Patina Engraving System, which engraves patina-like patterns on an activity tracker according to a user's activity logs. Using a piercing technique, the patina of activity logs has been made abstract, visually rich, gradually emerging, and historically accumulated. During the field trial, we found that the patina motivated the participants to increase exercises for engraving aesthetic patinas. A tracker with patina triggered spontaneous social interactions in face-to-face situations. The participants also cherished the trackers that held their own history. Based on the field trial, we discuss design implications for utilizing patina in designing future fashionable technologies.
Personality as a Predictor of User Strategy: How Locus of Control Affects Search Strategies on Tree Visualizations
Authors: Alvitta Ottley, Huahai Yang, Remco Chang
Published at CHI, 2015

Individual differences matter. While this has been the theme for many recent works in the Visualization and HCI communities, the mystery of how to develop personalized visualizations remains. This is largely because very little is known about how users actually use visualizations to solve problems and even less is known about how individual differences affect these problem-solving strategies. In this paper, we provide evidence that strategies are indeed influenced by individual differences. We demonstrate how the personality trait locus of control impacts strategies on hierarchical visualizations, and we introduce design recommendations for personalized visualizations.
SketchSliders: Sketching Widgets for Visual Exploration on Wall Displays
Authors: Theophanis Tsandilas, Anastasia Bezerianos, Thibaut Jacob
Published at CHI, 2015

We introduce a mobile sketching interface for exploring multi-dimensional datasets on wall displays. We demonstrate the idea of SketchSliders, range sliders that users can freely sketch on a mobile surface to customize their exploration. A small combination of sketches and gestures allows the creation of complex interactive sliders, such as circular sliders for periodic data, slider branches for detailed interaction, and fisheye transformation sliders. We augment sliders with a suite of tools, such as markers, slider cursors, and approximate views of data distributions. Our designs are inspired by a design study with three visualization experts and validated through a user study with six experts using our system. Our findings indicate that our sketching interface accommodates a wide range of exploration strategies, helping users customize as well as focus their visual explorations.