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.

There are mixed findings with respect to individual or gender differences in virtual Morris water maze tasks, which may be attributed to variations in the scale of the space, the cues provided, and differences in spatial navigation experience and abilities. We explore the question of scale and context by presenting participants with either a large (146 m) or small (36 m) outdoor virtual Morris maze, along with a measure of lifetime mobility and mental rotation skills. Results of this study suggest that, for the small-scale environment, males and females performed similarly when asked to navigate with only close visual cues. However, males outperformed females when only far cues were visible. In the large-scale environment, males outperformed females in both cue conditions. Additionally, mental rotation abilities predicted better navigation performance with close cues only. Finally, we found that highly mobile females and males perform equally well when navigating with close cues.

[This corrects the article DOI: 10.1186/s41235-018-0120-9.].

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.

Data visualizations are indispensable to modern scientific communication. As such, improving graph comprehension is a crucial target for STEM education. Unfortunately, there is no strong agreement on the components of graph comprehension nor how to measure it reliably, partly because no studies have compared the different measures directly. Here we administered two common graph comprehension assessments to the same individuals (N = 1,140) and analyzed their performance and error patterns. Our results suggest that these assessments measure a suite of abilities rather than a single construct. However, these abilities do not correspond to the categories that guided the design of either test. We find convergence between results obtained in a U.S. university and a U.S. demographically-representative sample, including an association between test performance and formal mathematics training. These findings raise fundamental questions concerning the mental representations that account for detailed patterns in how people answer questions using graphs.

Visualizations-visual representations of information, depicted in graphics-are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.

AbstractThe social impacts of natural resource management are challenging to evaluate because their perceived benefits and costs vary across stakeholder groups. Nevertheless, ensuring social acceptance is essential to building public support for adaptive measures required for the sustainable management of ecosystems in a warming climate. Based on surveys with both members of the public and natural-resource professionals in California, we applied structural-equation modeling to examine how psychological factors impact individuals' attitudes toward management's capacity to reduce the impacts of disturbance events, including wildfires, smoke from wildfires, drought, water shortages, tree mortality, and utility failure. We found the members of the public more optimistic than natural-resource professionals, perceiving management capacity to be on average 3.04 points higher (of 10) and displaying higher levels of trust of the government on both the state (Δ =11%) and federal levels (Δ =19%). Personal experience with natural-resource events had a positive effect on perceived management in both the public (1.26) and the professional samples (5.05), whereas perceived future risk had a negative effect within both samples (professional = -0.91, public = -0.45). In addition, higher trust and perceived management effectiveness were also linked with higher perceptions of management capacity in the public sample (1.81 versus 1.24), which could affect the acceptance of management actions. Continued social acceptance in a period of increasing risk may depend on managers sharing personal experiences and risk perception when communicating with the public. The contemporary shift toward multi-benefit aims is an important part of that message.

Data ensembles are often used to infer statistics to be used for a summary display of an uncertain prediction. In a spatial context, these summary displays have the drawback that when uncertainty is encoded via a spatial spread, display glyph area increases in size with prediction uncertainty. This increase can be easily confounded with an increase in the size, strength or other attribute of the phenomenon being presented. We argue that by directly displaying a carefully chosen subset of a prediction ensemble, so that uncertainty is conveyed implicitly, such misinterpretations can be avoided. Since such a display does not require uncertainty annotation, an information channel remains available for encoding additional information about the prediction. We demonstrate these points in the context of hurricane prediction visualizations, showing how we avoid occlusion of selected ensemble elements while preserving the spatial statistics of the original ensemble, and how an explicit encoding of uncertainty can also be constructed from such a selection. We conclude with the results of a cognitive experiment demonstrating that the approach can be used to construct storm prediction displays that significantly reduce the confounding of uncertainty with storm size, and thus improve viewers' ability to estimate potential for storm damage.

Uncertainty represented in visualizations is often ignored or misunderstood by the non-expert user. The National Hurricane Center displays hurricane forecasts using a track forecast cone, depicting the expected track of the storm and the uncertainty in the forecast. Our goal was to test whether different graphical displays of a hurricane forecast containing uncertainty would influence a decision about storm characteristics. Participants viewed one of five different visualization types. Three varied the currently used forecast cone, one presented a track with no uncertainty, and one presented an ensemble of multiple possible hurricane tracks. Results show that individuals make different decisions using uncertainty visualizations with different visual properties, demonstrating that basic visual properties must be considered in visualization design and communication.