A review of uncertainty visualization errors: Working memory as an explanatory theory

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of s lope (i.e., accepting a shown line). Notably, we found bias toward slopes that are "too steep" in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

Multimodality matters in numerical communication

Modern society depends on numerical information, which must be communicated accurately and effectively. Numerical communication is accomplished in different modalities-speech, writing, sign, gesture, graphs, and in naturally occurring settings it almost always involves more than one modality at once. Yet the modalities of numerical communication are often studied in isolation. Here we argue that, to understand and improve numerical communication, we must take seriously this multimodality. We first discuss each modality on its own terms, identifying their commonalities and differences. We then argue that numerical communication is shaped critically by interactions among modalities. We boil down these interactions to four types: one modality can amplify the message of another; it can direct attention to content from another modality (e.g., using a gesture to guide attention to a relevant aspect of a graph); it can explain another modality (e.g., verbally explaining the meaning of an axis in a graph); and it can reinterpret a modality (e.g., framing an upwards-oriented trend as a bad outcome). We conclude by discussing how a focus on multimodality raises entirely new research questions about numerical communication.

Probablement, Wahrscheinlich, Likely? A Cross-Language Study of How People Verbalize Probabilities in Icon Array Visualizations

Visualizations today are used across a wide range of languages and cultures. Yet the extent to which language impacts how we reason about data and visualizations remains unclear. In this paper, we explore the intersection of visualization and language through a cross-language study on estimative probability tasks with icon-array visualizations. Across Arabic, English, French, German, and Mandarin, n=50 participants per language both chose probability expressions - e.g. likely, probable - to describe icon-array visualizations (Vis-to-Expression), and drew icon-array visualizations to match a given expression (Expression-to-Vis). Results suggest that there is no clear one-to-one mapping of probability expressions and associated visual ranges between languages. Several translated expressions fell significantly above or below the range of the corresponding English expressions. Compared to other languages, French and German respondents appear to exhibit high levels of consistency between the visualizations they drew and the words they chose. Participants across languages used similar words when describing scenarios above 80% chance, with more variance in expressions targeting mid-range and lower values. We discuss how these results suggest potential differences in the expressiveness of language as it relates to visualization interpretation and design goals, as well as practical implications for translation efforts and future studies at the intersection of languages, culture, and visualization. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/g5d4r/.

Impact of COVID-19 forecast visualizations on pandemic risk perceptions

People worldwide use SARS-CoV-2 (COVID-19) visualizations to make life and death decisions about pandemic risks. Understanding how these visualizations influence risk perceptions to improve pandemic communication is crucial. To examine how COVID-19 visualizations influence risk perception, we conducted two experiments online in October and December of 2020 (N = 2549) where we presented participants with 34 visualization techniques (available at the time of publication on the CDC’s website) of the same COVID-19 mortality data. We found that visualizing data using a cumulative scale consistently led to participants believing that they and others were at more risk than before viewing the visualizations. In contrast, visualizing the same data with a weekly incident scale led to variable changes in risk perceptions. Further, uncertainty forecast visualizations also affected risk perceptions, with visualizations showing six or more models increasing risk estimates more than the others tested. Differences between COVID-19 visualizations of the same data produce different risk perceptions, fundamentally changing viewers’ interpretation of information.

Examining Effort in 1D Uncertainty Communication Using Individual Differences in Working Memory and NASA-TLX

As uncertainty visualizations for general audiences become increasingly common, designers must understand the full impact of uncertainty communication techniques on viewers' decision processes. Prior work demonstrates mixed performance outcomes with respect to how individuals make decisions using various visual and textual depictions of uncertainty. Part of the inconsistency across findings may be due to an over-reliance on task accuracy, which cannot, on its own, provide a comprehensive understanding of how uncertainty visualization techniques support reasoning processes. In this work, we advance the debate surrounding the efficacy of modern 1D uncertainty visualizations by conducting converging quantitative and qualitative analyses of both the effort and strategies used by individuals when provided with quantile dotplots, density plots, interval plots, mean plots, and textual descriptions of uncertainty. We utilize two approaches for examining effort across uncertainty communication techniques: a measure of individual differences in working-memory capacity known as an operation span (OSPAN) task and self-reports of perceived workload via the NASA-TLX. The results reveal that both visualization methods and working-memory capacity impact participants' decisions. Specifically, quantile dotplots and density plots (i.e., distributional annotations) result in more accurate judgments than interval plots, textual descriptions of uncertainty, and mean plots (i.e., summary annotations). Additionally, participants' open-ended responses suggest that individuals viewing distributional annotations are more likely to employ a strategy that explicitly incorporates uncertainty into their judgments than those viewing summary annotations. When comparing quantile dotplots to density plots, this work finds that both methods are equally effective for low-working-memory individuals. However, for individuals with high-working-memory capacity, quantile dotplots evoke more accurate responses with less perceived effort. Given these results, we advocate for the inclusion of converging behavioral and subjective workload metrics in addition to accuracy performance to further disambiguate meaningful differences among visualization techniques.