Can We Trust This Chart? (Asking for a Friend)bookmark_border

Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embodied pedagogical approach, in which viewers observe the dynamic hand-drawing of a visualization, can mitigate confirmation bias and improve interpretation accuracy. We conducted a study comparing static bar charts to videos in which charts are constructed through hand drawing, across contexts that either align with or challenge participants’ prior beliefs. The results indicate that hand-drawn videos helped participants accurately interpret data, even when the data conflicted with their prior beliefs. This approach also reduced belief-consistent errors and increased belief-overriding responses. These findings suggest that exposing the construction process of a visualization supports more accurate reasoning and mitigates the influence of confirmation bias. Consequently, this work introduces a promising design space for bias-mitigating data interfaces.

Status quo effects refer to an increased likelihood of selecting a default option over available alternatives. Although widely documented in behavioral research, their role in visualization-supported decision making remains unclear. We examine default adherence in probabilistic medical treatment decisions while systematically varying visualization format (quantile dotplots, density plots, probability bars, hypothetical outcome plots), default quality (good, average, or poor expected value), time pressure, and gain-loss framing. In a mixed-factorial experiment (N = 123), visualization was manipulated between participants and the remaining factors within participants. Default adherence was analyzed using mixed-effects logistic regression; decision accuracy (normalized expected value of the selected option) and response time were analyzed as complementary outcomes. Default adherence increased with default quality. Visualization format showed a marginal main effect on default adherence, though the observed differences appeared to be driven by specific visualization-default quality interactions under time pressure. Gain-loss framing showed no reliable association with default selection. Overall, our results suggest that choice-architecture factors such as defaults and time pressure can shape decision behavior in ways distinct from probabilistic decision accuracy. Supplementary material: https://osf.io/349jh/overview?view_only=89adaaaee52b4b4eb567dc5f05ffe058

Analyzing ensembles of contour trees is challenging due to variability in both topology and node attributes across instances. We present a framework for summarizing and visualizing such ensembles via approximate barycenter contour trees computed using the Fused Partial Gromov-Wasserstein (FPGW) distance. These barycenters provide representative summaries that capture shared structural patterns while supporting partial correspondences for unmatched or transient features. Our approach first estimates a representative barycenter matrix under the FPGW formulation and then reconstructs an approximate contour tree from this matrix. We further introduce a visualization that integrates the structural summary with transport-induced uncertainty. Specifically, we encode variability in function values, spatial embedding, and node correspondences, and propose a MetroSets-inspired view to expose structural differences among ensemble members. We demonstrate the effectiveness of our method on flow simulation ensembles, where barycenter contour trees yield interpretable summaries of topological variability and highlight regions of structural and scalar uncertainty grounded in the underlying data.

The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers need to consider the implicit or explicit rhetorics of their work, and beware of the potential of visualizations to imbue models with unearned trust. Supplemental material is available at https://osf.io/wufqz/}{https://osf.io/wufqz/.

Implicit neural representations (INRs) offer compact encoding of volumes, but as lossy approximators, inevitably have prediction errors. We consider INRs that can simultaneously encode relative error scales by predicting distributions using tools from uncertainty estimation. Typically, uncertainty estimation relies on computationally expensive approaches or on predefined parametric assumptions about the predictive distribution (e.g., Gaussian). In this study, we propose a lightweight method that reformulates regression-based INR training as a classification task by discretizing continuous targets into bins, enabling flexible distribution modeling to capture complex multimodal behaviors. We analyze the trade-off between regression and classification for INR training and demonstrate that the classification setting tends to achieve high reconstruction quality and competitive error awareness through uncertainty estimation, compared to regression-based approaches.

Differential privacy (DP) is increasingly adopted to protect sensitive data in visualization, but understanding how privacy-induced noise affects visual representations remains fragmented. Prior work has examined the impact of DP noise on specific chart types, yet these findings are typically tied to particular chart types and evaluation settings, making it difficult to generalize across contexts. In parallel, visualization research has long established that visual channels, the visual properties of marks that encode data attributes, differ in perceptual effectiveness under noise-free conditions. However, these principles have not been systematically revisited under DP, where noise is deliberately introduced into the data to protect privacy. In this paper, we present a channel-centric perspective to reframe prior empirical and theoretical results. Our synthesis drawson prior findings to motivate a channel-read-mechanism hypothesis: the utility of DP visualizations may depend on the task-relevant reads made salient by visual channel configurations and DP mechanism choice jointly. Moreover, this perspective highlights the role of channel choice throughout the privacy-preserving visualization pipeline, from disclosure framing and DP algorithm design to utility evaluation and interpretation of noisy outputs. The synthesis also identifies gaps in the existing evaluation literature and highlights research opportunities for channel-aware DP visualization.

An important task of uncertainty visualizations is to communicate how the number of available observations influences the precision of estimates, such as a Normal distribution’s mean. In this paper, we propose an adaptive approach to visualizing univariate data that produces salient representations while highlighting the uncertainty introduced by the sample size. We introduce jk-plots, inspired by educational material of Bayesian statistics, that combine visualizations of plausible reference models (emulating popular uncertainty visualization idioms) and the observations. We describe a visualization pipeline to create jk-plots, which we implemented in an R package. We demonstrate jk-plots using synthetic datasets of varying size and discuss possible usage scenarios in domain expert interviews.

Color-encoded scalar field visualizations are fundamental to scientific data analysis, yet their interpretation depends critically on both data characteristics and colormap design. While multimodal large language models (MLLMs) have emerged as promising tools for visualization tasks, their ability to comprehend and interpret scalar field visualizations remains largely unexplored. We investigate how effectively MLLMs perceive these visualizations without direct access to the underlying data values, focusing on two graphical perception tasks: value identification and gradient comparison. Building on the well-established two-step human perception workflow—legend understanding and linking legend to visualization—we develop a structured prompting framework for MLLM perception workflows and implement it through both decomposed-step and chain-of-thought (CoT) prompting strategies across five state-of-the-art MLLMs, alongside fine-tuning techniques for model adaptation. Our results reveal a model-dependent interaction between prompting and model capacity: CoT prompting benefits weaker models (e.g., reducing Task 2 error by up to 21.5%) but degrades stronger ones (relative error increases of up to 84.76% on Task 1 for the strongest models we tested), while fine-tuning yields consistent improvements (up to a 43.80% relative error reduction across both tasks). Our work bridges human and machine perception workflows, demonstrating the promising potential of leveraging MLLMs to understand graphical perception while highlighting the need for model-aware prompting strategies in visualization perception.