Abstract Immersive virtual reality (IVR) is increasingly used for scientific data visualization, with the expectation that greater immersion will enhance both engagement and understanding. However, prior research suggests a potential trade‐off: IVR can heighten affective responses while impairing cognitive learning due to increased cognitive load. To examine this tension in abstract scientific visualization, we conducted a between‐subjects study (N=84) comparing head‐mounted display (HMD) VR with traditional desktop displays as participants explored three climate datasets on an interactive 3D globe. We measured cognitive and affective learning outcomes after each exploration trial and at a two‐week follow‐up. We additionally logged patterns of visualization exploration during trials, and measured user experience post‐study and at two‐week follow‐up. Results reveal systematic divergences: desktop displays led to significantly higher recall accuracy and promoted more systematic exploration patterns, while HMD‐based VR produced stronger short‐term increases in climate concern and higher satisfaction ratings. Critically, affective changes largely reverted to baseline at follow‐up regardless of modality. We further identify five distinct exploration strategies that emerge differentially across modalities and relate directly to learning outcomes. Overall, our findings highlight how modality choice should align with visualization goals, and offer actionable insights into designing effective scientific visualizations that balance cognitive and affective learning objectives. All data and materials are available at: https://osf.io/24w7s/ .

Color is a powerful tool in data visualization, but for individuals with color vision deficiencies (CVD), hue can become a barrier rather than an aid. In this paper, we examine how real-world visualizations are perceived across vision profiles through three complementary studies. Study 1 assessed how normal vision participants rated 46 visualizations shown in original and simulated red/green colorblind versions. Study 2 collected matched responses from participants with diagnosed CVD. Study 3 involved in-depth interviews exploring how users interpret, adapt to, and evaluate inaccessible designs. Across studies, we find that simulations capture directional perceptual shifts but fail to reflect the interpretive breakdowns and emotional work described by real CVD users. Factor analysis reveals two dominant perceptual dimensions: functional utility and affective experience. While normal vision participants prioritize functional clarity, CVD users rely more on structural cues and emotional resonance, particularly when color is unreliable. Qualitative insights show that perceptual breakdowns occur not only in high-interference charts but also when redundant encoding or layout scaffolding is missing. We synthesize these findings and offer empirically grounded design recommendations to guide inclusive visualization practices. Our results argue that accessibility must go beyond color correction, embracing structural clarity, redundancy, and real-user validation to ensure inclusive visual communication.

Text is an integral but understudied component of visualization design. Although recent studies have examined how text elements (e.g., titles and annotations) influence comprehension, preferences, and predictions, many questions remain about textual design and use in practice. This paper introduces a framework for understanding text functions in information visualizations, building on and filling gaps in prior classifications and taxonomies. Through an analysis of 120 real-world visualizations and 804 text elements, we identified ten distinct text functions, ranging from identifying data mappings to presenting valenced subtext. We further identify patterns in text usage and conduct a factor analysis, revealing four overarching text-informed design strategies: Attribution and Variables, Annotation-Centric Design, Visual Embellishments, and Narrative Framing. In addition to these factors, we explore features of title rhetoric and text multifunctionality, while also uncovering previously unexamined text functions, such as text replacing visual elements. Our findings highlight the flexibility of text, demonstrating how different text elements in a given design can combine to communicate, synthesize, and frame visual information. This framework adds important nuance and detail to existing frameworks that analyze the diverse roles of text in visualization.

Abstract Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long‐term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short‐term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative‐driven insights and less informative ones prompting aesthetic‐driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/ .

User experience in data visualization is typically assessed through post-viewing self-reports, but these overlook the dynamic cognitive processes during interaction. This study explores the use of mind wandering- a phenomenon where attention spontaneously shifts from a primary task to internal, task-related thoughts or unrelated distractions- as a dynamic measure during visualization exploration. Participants reported mind wandering while viewing visualizations from a pre-labeled visualization database and then provided quantitative ratings of trust, engagement, and design quality, along with qualitative descriptions and short-term/long-term recall assessments. Results show that mind wandering negatively affects short-term visualization recall and various post-viewing measures, particularly for visualizations with little text annotation. Further, the type of mind wandering impacts engagement and emotional response. Mind wandering also functions as an intermediate process linking visualization design elements to post-viewing measures, influencing how viewers engage with and interpret visual information over time. Overall, this research underscores the importance of incorporating mind wandering as a dynamic measure in visualization design and evaluation, offering novel avenues for enhancing user engagement and comprehension.

Large language models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc natural language processing (NLP) tasks with simple natural language prompts. Part of the appeal for LLMs is their approachability to the general public, including individuals with little technical expertise in NLP. However, prompts can vary significantly in terms of their linguistic structure, context, and other semantics, and modifying one or more of these aspects can result in significant differences in task performance. Non-expert users may find it challenging to identify the changes needed to improve a prompt, especially when they lack domain-specific knowledge and appropriate feedback. To address this challenge, we present PromptAid, a visual analytics system designed to interactively create, refine, and test prompts through exploration, perturbation, testing, and iteration. PromptAid uses coordinated visualizations which allow users to improve prompts via three strategies: keyword perturbations, paraphrasing perturbations, and obtaining the best set of in-context few-shot examples. PromptAid was designed through a pre-study involving NLP experts, and evaluated via a robust mixed-methods user study. Our findings indicate that PromptAid helps users to iterate over prompts with less cognitive overhead, generate diverse prompts with the help of recommendations, and analyze the performance of the generated prompts while surpassing existing state-of-the-art prompting interfaces in performance.

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image (aims to provide visual references, evoke emotions, express creativity, and inspire philosophic thought) or as information (aims to present complex data, information, or ideas concisely and promote analytical thinking)? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as 'image' or 'information.' We then conducted an in-person study ( n = 101) using a free recall task to examine how the image/information ratings and design elements impacted memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically 'appealing,' 'enjoyable,' and 'pleasing.' Information-rated visualizations were perceived as less 'difficult to understand' and more aesthetically 'likable' and 'nice,' though participants expressed higher 'positive' sentiment when viewing image-rated visualizations and felt less 'guided to a conclusion.' The presence of axes and text annotations heavily influenced the likelihood of participants rating the visualization as 'information.' We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as 'images' are less effective in conveying trends and messages, though they elicit a more positive emotional judgment, while 'informative' visualizations exhibit annotation focused recall and elicit a more positive design judgment. We discuss the implications of this dissociation between aesthetic pleasure and perceived ease of use in visualization design.

Recent research has shown that language models exploit 'artifacts' in benchmarks to solve tasks, rather than truly learning them, leading to inflated model performance.In pursuit of creating better benchmarks, we propose VAIDA, a novel benchmark creation paradigm for NLP, that focuses on guiding crowdworkers, an under-explored facet of addressing benchmark idiosyncrasies.VAIDA facilitates sample correction by providing real-time visual feedback and recommendations to improve sample quality.Our approach is domain, model, task, and metric agnostic, and constitutes a paradigm shift for robust, validated, and dynamic benchmark creation via human-and-metric-in-theloop workflows.We evaluate via expert review and a user study with NASA TLX.We find that VAIDA decreases effort, frustration, mental, and temporal demands of crowdworkers and analysts, simultaneously increasing the performance of both user groups with a 45.8% decrease in the level of artifacts in created samples.As a by-product of our user study, we observe that created samples are adversarial across models, leading to decreases of 31.3% (BERT), 22.5% (RoBERTa), 14.98% (GPT-3 fewshot) in performance.1

Augmented reality (AR) provides a significant opportunity to improve collaboration between co-located team members jointly analyzing data visualizations, but existing rigorous studies are lacking. We present a novel method for qualitatively encoding the positions of co-located users collaborating with head-mounted displays (HMDs) to assist in reliably analyzing collaboration styles and behaviors. We then perform a user study on the collaborative behaviors of multiple, co-located synchronously collaborating users in AR to demonstrate this method in practice and contribute to the shortfall of such studies in the existing literature. Pairs of users performed analysis tasks on several data visualizations using both AR and traditional desktop displays. To provide a robust evaluation, we collected several types of data, including software logging of participant positioning, qualitative analysis of video recordings of participant sessions, and pre- and post-study questionnaires including the NASA TLX survey. Our results suggest that the independent viewports of AR headsets reduce the need to verbally communicate about navigating around the visualization and encourage face-to-face and non-verbal communication. Our novel positional encoding method also revealed the overlap of task and communication spaces vary based on the needs of the collaborators.

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.

Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Models that top leaderboards often perform unsatisfactorily when deployed in real world applications; this has necessitated rigorous and expensive pre-deployment model testing. A hitherto unexplored facet of model performance is: Are our leaderboards doing equitable evaluation? In this paper, we introduce a task-agnostic method to probe leaderboards by weighting samples based on their 'difficulty' level. We find that leaderboards can be adversarially attacked and top performing models may not always be the best models. We subsequently propose alternate evaluation metrics. Our experiments on 10 models show changes in model ranking and an overall reduction in previously reported performance- thus rectifying the overestimation of AI systems' capabilities. Inspired by behavioral testing principles, we further develop a prototype of a visual analytics tool that enables leaderboard revamping through customization, based on an end user's focus area. This helps users analyze models' strengths and weaknesses, and guides them in the selection of a model best suited for their application scenario. In a user study, members of various commercial product development teams, covering 5 focus areas, find that our prototype reduces pre-deployment development and testing effort by 41% on average.