DevLan/vispubs huggingface dataset:
An Eye Gaze Heatmap Analysis of Uncertainty Head-Up Display Designs for Conditional Automated Driving
Authors: Michael A. Gerber, Ronald Schroeter, Daniel Johnson 0001, Christian P. Janssen, Andry Rakotonirainy, Jonny Kuo, Mike Lenné
Published at CHI, 2024

This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle's dynamic "uncertainty" about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty close to the video), interruption (interrupting the video during uncertain situations), and combination (a combination of both), against a baseline (video-only). We visualised eye-tracking data to conduct a heatmap analysis of the four groups' gaze behaviour over time. We found interruptions initiated a phase during which participants interleaved their attention between monitoring and entertainment. This improved monitoring behaviour was more pronounced in combination compared to interruption, suggesting pre-warning interruptions have positive effects. The same addition had negative effects without interruptions (comparing baseline & display). Intermittent interruptions may have safety benefits over placing additional peripheral displays without compromising usability.
Better Little People Pictures: Generative Creation of Demographically Diverse Anthropographics
Authors: Priya Dhawka, Lauren Perera, Wesley Willett
Published at CHI, 2024

We explore the potential of generative AI text-to-image models to help designers efficiently craft unique, representative, and demographically diverse anthropographics that visualize data about people. Currently, creating data-driven iconic images to represent individuals in a dataset often requires considerable design effort. Generative text-to-image models can streamline the process of creating these images, but risk perpetuating designer biases in addition to stereotypes latent in the models. In response, we outline a conceptual workflow for crafting anthropographic assets for visualizations, highlighting possible sources of risk and bias as well as opportunities for reflection and refinement by a human designer. Using an implementation of this workflow with Stable Diffusion and Google Colab, we illustrate a variety of new anthropographic designs that showcase the visual expressiveness and scalability of these generative approaches. Based on our experiments, we also identify challenges and research opportunities for new AI-enabled anthropographic visualization tools.
CharacterMeet: Supporting Creative Writers' Entire Story Character Construction Processes Through Conversation with LLM-Powered Chatbot Avatars
Authors: Hua Xuan Qin, Shan Jin, Ze Gao, Mingming Fan 0001, Pan Hui 0001
Published at CHI, 2024

Support for story character construction is as essential as characters are for stories. Building upon past research on early character construction stages, we explore how conversation with chatbot avatars embodying characters powered by more recent technologies could support the entire character construction process for creative writing. Through a user study (N=14) with creative writers, we examine thinking and usage patterns of CharacterMeet, a prototype system allowing writers to progressively manifest characters through conversation while customizing context, character appearance, voice, and background image. We discover that CharacterMeet facilitates iterative character construction. Specifically, participants, including those with more linear usual approaches, alternated between writing and personalized exploration through visualization of ideas on CharacterMeet while visuals and audio enhanced immersion. Our findings support research on iterative creative processes and the growing potential of personalizable generative AI creativity support tools. We present design implications for leveraging chatbot avatars in the creative writing process.
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
Authors: Matt-Heun Hong, Zachary Nolan Sunberg, Danielle Albers Szafir
Published at CHI, 2024

Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that allows any data analyst to rapidly find quality colormaps while designing charts within Jupyter Notebooks. Our system employs an active preference learning paradigm to rank expert-designed colormaps and create new ones from pairwise comparisons, allowing analysts who are novices in color design to tailor colormaps to their data context. We accomplish this by treating colormap design as a path planning problem through the CIELAB colorspace with a context-specific reward model. In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs. Our work shows the potential of active preference learning for supporting efficient visualization design optimization.
Co-designing Customizable Clinical Dashboards with Multidisciplinary Teams: Bridging the Gap in Chronic Disease Care
Authors: Diogo Branco, Margarida Móteiro, Raquel Bouça-Machado, Rita Miranda, Tiago Reis, Élia Decoroso, Rita Cardoso, Joana Ramalho, Filipa Rato, Joana Malheiro, Diana Miranda, Verónica Caniça, Filipa Pona-Ferreira, Daniela Guerreiro, Mariana Leitão, Alexandra Saúde Braz, Joaquim J. Ferreira, Tiago João Guerreiro
Published at CHI, 2024

Providing care to individuals with chronic diseases benefits from a multidisciplinary approach and longitudinal symptom, event, and disease monitoring, in and out of clinical facilities. Technological advancements, including the ubiquitous presence of sensors and devices, present opportunities to collect large amounts of data and extract evidence-based insights about the patient and disease. Nevertheless, practical examples of clinical utility of those technologies remain sparse, and in specific focus areas (e.g, insights from a single device). This paper explores the challenges and opportunities of multidisciplinary clinical dashboards to support clinicians caring for people with chronic diseases. We report on a focus group and co-design workshops with a multidisciplinary team of clinicians and HCI researchers. We offer insights into how technological outcomes and visualizations can enhance clinical practice and the intricacies of information-sharing dynamics. We discuss the potential of dashboards to trigger actions in clinical settings and emphasize the benefits of customizable dashboards.
CollageVis: Rapid Previsualization Tool for Indie Filmmaking using Video Collages
Authors: Hye-Young Jo, Ryo Suzuki 0001, Yoonji Kim
Published at CHI, 2024

Previsualization, previs, is essential for film production, allowing cinematographic experiments and effective collaboration. However, traditional previs methods like 2D storyboarding and 3D animation require substantial time, cost, and technical expertise, posing challenges for indie filmmakers. We introduce CollageVis, a rapid previsualization tool using video collages. CollageVis enables filmmakers to create previs through two main user interfaces. First, it automatically segments actors from videos and assigns roles using name tags, color filters, and face swaps. Second, it positions video layers on a virtual stage and allows users to record shots using mobile as a proxy for a virtual camera. These features were developed based on formative interviews by reflecting indie filmmakers' needs and working methods. We demonstrate the system's capability by replicating seven film scenes and evaluate the system's usability with six indie filmmakers. The findings indicate that CollageVis allows more flexible yet expressive previs creation for idea development and collaboration.
Comparison of Spatial Visualization Techniques for Radiation in Augmented Reality
Authors: Fintan McGee, Roderick McCall, Joan Baixauli
Published at CHI, 2024

Augmented Reality (AR) provides a safe and low-cost option for hazardous safety training that allows for the visualization of aspects that may be invisible, such as radiation. Effectively visually communicating such threats in the environment around the user is not straightforward. This work describes visually encoding radiation using the spatial awareness mesh of an AR Head Mounted Display. We leverage the AR device's GPUs to develop a real time solution that accumulates multiple dynamic sources and uses stencils to prevent an environment being over saturated with a visualization, as well as supporting the encoding of direction explicitly in the visualization. We perform a user study (25 participants) of different visualizations and obtain user feedback. Results show that there are complex interactions and while no visual representation was statistically superior or inferior, user opinions vary widely. We also discuss the evaluation approaches and provide recommendations.
Damage Optimization in Video Games: A Player-Driven Co-Creative Approach
Authors: Johannes Pfau, Manik Charan, Erica Kleinman, Magy Seif El-Nasr
Published at CHI, 2024

The concept of dealing damage is established and widespread in video games. With growing complexity and countless interactions in modern games, capturing how damage unfolds becomes an intricate problem - for developers just as for players. Misunderstanding how to optimize damage potentials includes risks of game imbalances, game-breaking exploits, mismatches between player skill and challenge (harming flow), and impaired perceived competence. All of these considerably impact player experience, game reception, success, and retention, yet polishing optimal strategies remains often a player community effort. To accelerate, inform and ease this process, we implemented an interactive tool capable of simulating, visualizing, planning and comparing damage strategies in video games. Following a case study within the Guild Wars 2 community, we contribute a player-driven perspective on the problem of damage optimization, as well as an artifact that resulted in empirical improvements – advancing the fields of game analytics, game evaluation methods and self-regulated learning.
Data Cubes in Hand: A Design Space of Tangible Cubes for Visualizing 3D Spatio-Temporal Data in Mixed Reality
Authors: Shuqi He, Haonan Yao, Luyan Jiang, Kaiwen Li, Nan Xiang, Yue Li 0023, Hai-Ning Liang, Lingyun Yu 0001
Published at CHI, 2024

Tangible interfaces in mixed reality (MR) environments allow for intuitive data interactions. Tangible cubes, with their rich interaction affordances, high maneuverability, and stable structure, are particularly well-suited for exploring multi-dimensional data types. However, the design potential of these cubes is underexplored. This study introduces a design space for tangible cubes in MR, focusing on interaction space, visualization space, sizes, and multiplicity. Using spatio-temporal data, we explored the interaction affordances of these cubes in a workshop (N=24). We identified unique interactions like rotating, tapping, and stacking, which are linked to augmented reality (AR) visualization commands. Integrating user-identified interactions, we created a design space for tangible-cube interactions and visualization. A prototype visualizing global health spending with small cubes was developed and evaluated, supporting both individual and combined cube manipulation. This research enhances our grasp of tangible interaction in MR, offering insights for future design and application in diverse data contexts.
Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work
Authors: Angie Zhang, Rocita Rana, Alexander Boltz, Veena Dubal, Min Kyung Lee
Published at CHI, 2024

Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated workers' experiences under algorithmic management and how to design interventions, rarely are the perspectives of stakeholders who inform or craft policy sought. To bridge this, we propose using data probes—interactive visualizations of workers' data that show the impact of technology practices on people—exploring them in 12 semi-structured interviews with policy informers, (driver-)organizers, litigators, and a lawmaker in the rideshare space. We show how data probes act as boundary objects to assist stakeholder interactions, demystify technology for policymakers, and support worker collective action. We discuss the potential for data probes as training tools for policymakers, and considerations around data access and worker risks when using data probes.
Data Storytelling in Data Visualisation: Does it Enhance the Efficiency and Effectiveness of Information Retrieval and Insights Comprehension?
Authors: Hongbo Shao, Roberto Martínez-Maldonado, Vanessa Echeverría, Lixiang Yan, Dragan Gasevic
Published at CHI, 2024

Data storytelling (DS) is rapidly gaining attention as an approach that integrates data, visuals, and narratives to create data stories that can help a particular audience to comprehend the key messages underscored by the data with enhanced efficiency and effectiveness. It is been posited that DS can be especially advantageous for audiences with limited visualisation literacy, by presenting the data clearly and concisely. However, empirical studies confirming whether data stories indeed provide these benefits over conventional data visualisations are scarce. To bridge this gap, we conducted a study with 103 participants to determine whether DS indeed improve both efficiency and effectiveness in tasks related to information retrieval and insights comprehension. Our findings suggest that data stories do improve the efficiency of comprehension tasks, as well as the effectiveness of comprehension tasks that involve a single insight, compared with conventional visualisations. Interestingly, these benefits were not associated with participants' visualisation literacy.
DeepSee: Multidimensional Visualizations of Seabed Ecosystems
Authors: Adam Coscia, Haley M. Sapers, Noah Deutsch, Malika Khurana, John S. Magyar, Sergio A. Parra, Daniel R. Utter, Rebecca L. Wipfler, David W. Caress, Eric J. Martin, Jennifer B. Paduan, Maggie Hendrie, Santiago V. Lombeyda, Hillary Mushkin, Alex Endert, Scott Davidoff, Victoria J. Orphan
Published at CHI, 2024

Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.
Discovering Accessible Data Visualizations for People with ADHD
Authors: Tien Tran, Hae Na Lee, Ji Hwan Park
Published at CHI, 2024

There have been many studies on understanding data visualizations regarding general users. However, we have a limited understanding of how people with ADHD comprehend data visualizations and how it might be different from the general users. To understand accessible data visualization for people with ADHD, we conducted a crowd-sourced survey involving 70 participants with ADHD and 77 participants without ADHD. Specifically, we tested the chart components of color, text amount, and use of visual embellishments/pictographs, finding that some of these components and ADHD affected participants' response times and accuracy. We outlined the neurological traits of ADHD and discussed specific findings on accessible data visualizations for people with ADHD. We found that various chart embellishment types affected accuracy and response times for those with ADHD differently depending on the types of questions. Based on these results, we suggest visual design recommendations to make accessible data visualizations for people with ADHD.
Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension
Authors: Ghulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno Nieves, Paul Rosen 0001, Danielle Albers Szafir
Published at CHI, 2024

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations—line graphs, bar graphs, and scatterplots—to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.
DoodleTunes: Interactive Visual Analysis of Music-Inspired Children Doodles with Automated Feature Annotation
Authors: Shuqi Liu, Jia Bu, Huayuan Ye, Juntong Chen, Shiqi Jiang, Mingtian Tao, Liping Guo, Changbo Wang, Chenhui Li
Published at CHI, 2024

Music and visual arts are essential in children's arts education, and their integration has garnered significant attention. Existing data analysis methods for exploring audio-visual correlations are limited. Yet, relevant research is necessary for innovating and promoting arts integration courses. In our work, we collected substantial volumes of music-inspired doodles created by children and interviewed education experts to comprehend the challenges they encountered in the relevant analysis. Based on the insights we obtained, we designed and constructed an interactive visualization system DoodleTunes. DoodleTunes integrates deep learning-driven methods for automatically annotating several types of data features. The visual designs of the system are based on a four-level analysis structure to construct a progressive workflow, facilitating data exploration and insight discovery between doodle images and corresponding music pieces. We evaluated the accuracy of our feature prediction results and collected usage feedback on DoodleTunes from five domain experts.
Doorways Do Not Always Cause Forgetting: Studying the Effect of Locomotion Technique and Doorway Visualization in Virtual Reality
Authors: Thomas Van Gemert, Sean Chew, Yiannis Kalaitzoglou, Joanna Bergström
Published at CHI, 2024

The "doorway effect" predicts that crossing an environmental boundary affects memory negatively. In virtual reality (VR), we can design the crossing and the appearance of such boundaries in non-realistic ways. However, it is unclear whether locomotion techniques like teleportation, which avoid crossing the boundary altogether, still induce the effect. Furthermore, it is unclear how different appearances of a doorway act as a boundary and thus induce the effect. To address these questions, we conducted two lab studies. First, we conceptually replicated prior doorway effect studies in VR using natural walking and teleportation. Second, we investigated the effect of five doorway visualizations, ranging from doors to portals. The results show no difference in object recognition performance due to the presence of a doorway, locomotion technique, or doorway visualization. We discuss the implications of these findings on the role of boundaries in event-based memory and the design of boundary interactions in VR.
DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing
Authors: Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala, Chenglong Wang
Published at CHI, 2024

Users often rely on GUIs to edit and interact with visualizations — a daunting task due to the large space of editing options. As a result, users are either overwhelmed by a complex UI or constrained by a custom UI with a tailored, fixed subset of options with limited editing flexibility. Natural Language Interfaces (NLIs) are emerging as a feasible alternative for users to specify edits. However, NLIs forgo the advantages of traditional GUI: the ability to explore and repeat edits and see instant visual feedback. We introduce DynaVis, which blends natural language and dynamically synthesized UI widgets. As the user describes an editing task in natural language, DynaVis performs the edit and synthesizes a persistent widget that the user can interact with to make further modifications. Study participants (n=24) preferred DynaVis over the NLI-only interface citing ease of further edits and editing confidence due to immediate visual feedback.
Effects of Point Size and Opacity Adjustments in Scatterplots
Authors: Gabriel Strain, Andrew J. Stewart, Paul A. Warren, Caroline Jay
Published at CHI, 2024

Systematically changing the size and opacity of points on scatterplots can be used to induce more accurate perceptions of correlation by viewers. Evidence points to the mechanisms behind these effects being similar, so one may expect their combination to be additive regarding their effects on correlation estimation. We present a fully-reproducible study in which we combine techniques for influencing correlation perception to show that in reality, effects of changing point size and opacity interact in a non-additive fashion. We show that there is a great deal of scope for using visual features to change viewers' perceptions of data visualizations. Additionally, we use our results to further interrogate the perceptual mechanisms at play when changing point size and opacity in scatterplots.