Uncertain About Uncertainty: How Qualitative Expressions of Forecaster Confidence Impact Decision-Making With Uncertainty Visualizations.

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction between direct quantitative uncertainty and indirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how "good" a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants' decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty.

The powerful influence of marks: Visual and knowledge-driven processing in hurricane track displays.

Given the widespread use of visualizations to communicate hazard risks, forecast visualizations must be as effective to interpret as possible. However, despite incorporating best practices, visualizations can influence viewer judgments in ways that the designers did not anticipate. Visualization designers should understand the full implications of visualization techniques and seek to develop visualizations that account for the complexities in decision-making. The current study explores the influence of visualizations of uncertainty by examining a case in which ensemble hurricane forecast visualizations produce unintended interpretations. We show that people estimate more damage to a location that is overlapped by a track in an ensemble hurricane forecast visualization compared to a location that does not coincide with a track. We find that this effect can be partially reduced by manipulating the number of hurricane paths displayed, suggesting the importance of visual features of a display on decision making. Providing instructions about the information conveyed in the ensemble display also reduced the effect, but importantly, did not eliminate it. These findings illustrate the powerful influence of marks and their encodings on decision-making with visualizations. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Factors Predicting Willingness to Share COVID-19 Misinformation.

We conducted a preregistered exploratory survey to assess whether patterns of individual differences in political orientation, social dominance orientation (SDO), traditionalism, conspiracy ideation, or attitudes about science predict willingness to share different kinds of misinformation regarding the COVID-19 pandemic online. Analyses revealed two orthogonal models of individual differences predicting the willingness to share misinformation over social media platforms. Both models suggest a sizable role of different aspects of political belief, particularly SDO, in predicting tendencies to share different kinds of misinformation, predominantly conspiracy theories. Although exploratory, results from this study can contribute to the formulation of a socio-cognitive profile of individuals who act as vectors for the spread of scientific misinformation online, and can be useful for computationally modeling misinformation diffusion.

Toward Objective Evaluation of Working Memory in Visualizations: A Case Study Using Pupillometry and a Dual-Task Paradigm.

Cognitive science has established widely used and validated procedures for evaluating working memory in numerous applied domains, but surprisingly few studies have employed these methodologies to assess claims about the impacts of visualizations on working memory. The lack of information visualization research that uses validated procedures for measuring working memory may be due, in part, to the absence of cross-domain methodological guidance tailored explicitly to the unique needs of visualization research. This paper presents a set of clear, practical, and empirically validated methods for evaluating working memory during visualization tasks and provides readers with guidance in selecting an appropriate working memory evaluation paradigm. As a case study, we illustrate multiple methods for evaluating working memory in a visual-spatial aggregation task with geospatial data. The results show that the use of dual-task experimental designs (simultaneous performance of several tasks compared to single-task performance) and pupil dilation can reveal working memory demands associated with task difficulty and dual-tasking. In a dual-task experimental design, measures of task completion times and pupillometry revealed the working memory demands associated with both task difficulty and dual-tasking. Pupillometry demonstrated that participants' pupils were significantly larger when they were completing a more difficult task and when multitasking. We propose that researchers interested in the relative differences in working memory between visualizations should consider a converging methods approach, where physiological measures and behavioral measures of working memory are employed to generate a rich evaluation of visualization effort.