Designing Graphs to Communicate Risks: Understanding How the Choice of Graphical Format Influences Decision Making

Numerical versus graphical aids for decision-making in a multi-cue signal identification task

Decision aids are commonly used in tactical decision-making environments to help humans integrate base-rate and multi-cue information. However, it is important that users appropriately trust and rely on aids. Decision aids can be presented in many ways, but the literature lacks clarity over the conditions surrounding their effectiveness. This research aims to determine whether a numerical or graphical aid more effectively supports human performance, and explores the relationships between aid presentation, trust, and workload. Participants (N = 30) completed a signal-identification task that required integration of readings from a set of three dynamic gauges. Participants experienced three conditions: unaided, using a numerical aid, and using a graphical aid. The aids combined gauge and base-rate information in a statistically-optimal fashion. Participants also indicated how much they trusted the system and how hard they worked during the task. Analyses explored the impact of aid condition on sensitivity, response bias, response time, trust, and workload. Both the numerical and graphical aids produced significant increases in sensitivity and trust, and significant decreases in workload in comparison to the unaided condition. The difference in response time between the graphical and unaided conditions approached significance, with participants responding faster using the graphical aid without decrements in sensitivity. Significant interactions between aid and signal type indicated that both aided conditions promoted faster responding to non-hostile signals, with larger mean differences in the graphical aid condition. Practically, graphical aids in which suggestions are more salient to users may promote faster responding in tactical environments, with negligible cost of accuracy.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences

OBJECTIVES

To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).

MATERIALS AND METHODS

We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome.

RESULTS

Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%).

DISCUSSION AND CONCLUSION

All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Policies to influence perceptions about COVID-19 risk: The case of maps

Choropleth disease maps are often used to inform the public about the risks posed by coronavirus disease 2019 (COVID-19). In a survey conducted in the U.S. state of Georgia in June 2020, we randomly assigned respondents to view either of two maps. The first map reported county-level COVID case counts; the second displayed case rates per 100,000 people. Respondents who saw case rate maps were less likely to perceive COVID as mostly an urban problem and reported higher levels of concerns about the virus. Case rate maps also increased support for policies aimed at mitigating the spread of the virus, although, for this outcome, the effect was quantitatively small and the maps did not change individuals' self-reported behavior. For several outcomes, the impact of the case rate map was strongest for rural residents and self-identified Republicans, both of whom were less worried about the virus and more skeptical about public health measures to mitigate its spread.

Effects of icon arrays to communicate risk in a repeated risky decision-making task

In two experiments, participants decided on each of several trials whether or not to take a risk. If they chose to take the risk, they had a relatively high probability (from 75% to 95%) of winning a small number of points and a relatively low probability (5% to 25%) of losing a large number of points. The loss amounts varied so that the expected value of taking the risk was positive on some trials, zero on others, and negative on the rest. The main independent variable was whether the probability of losing was communicated using numerical percentages or icon arrays. Both experiments included random icon arrays, in which the icons representing losses were randomly distributed throughout the array. Experiment 2 also included grouped icon arrays, in which the icons representing losses were grouped at the bottom of the array. Neither type of icon array led to better performance in the task. However, the random icon arrays led to less risk taking than the numerical percentages or the grouped icon arrays, especially at the higher loss probabilities. In a third experiment, participants made direct judgments of the percentages and probabilities represented by the icon arrays from Experiment 2. The results supported the idea that random arrays lead to less risk taking because they are perceived to represent greater loss probabilities. These results have several implications for the study of icon arrays and their use in risk communication.

Determinants of information diffusion in online communication on vaccination: The benefits of visual displays

OBJECTIVE

Social media are an increasingly important source of information on the benefits and risks of vaccinations, but the high prevalence of misinformation provides challenges for informed vaccination decisions. It is therefore important to understand which messages are likely to diffuse online and why, and how relevant aspects-such as scientific facts on vaccination effectiveness-can be made more comprehensible and more likely to be shared. In two studies, we (i) explore which characteristics of messages on flu vaccination facilitate their diffusion in online communication, and (ii) whether visual displays (i.e., icon arrays) facilitate the comprehension and diffusion of scientific effectiveness information.

METHODS

In Study 1, 208 participants each rated a random sample of 15 out of 63 messages on comprehensibility, trustworthiness, persuasiveness, familiarity, informativeness, valence, and arousal, and then reported which information they would share with subsequent study participants. In Study 2 (N = 758), we employed the same rating procedure for a selected set of 9 messages and experimentally manipulated how scientific effectiveness information was displayed.

RESULTS

Study 1 illustrated that scientific effectiveness information was difficult to understand and thus did not diffuse well. Study 2 demonstrated that visual displays improved the understanding of this information, which could, in turn, increase its social impact.

CONCLUSIONS

The comprehensibility of scientific information is an important prerequisite for its diffusion. Visual displays can facilitate informed vaccination decisions by rendering important information on vaccination effectiveness more transparent and increasing the willingness to share this information.

Visual analogies, not graphs, increase patients' comprehension of changes in their health status

OBJECTIVES

Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs.

MATERIALS AND METHODS

We conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4) text plus line graph. Each participant viewed every condition, and we used counterbalancing (systematic randomization) to control for potential order effects. We assessed objective comprehension using the International Organization for Standardization protocol. Secondary outcomes included response times, preferences, risk perceptions, and behavioral intentions.

RESULTS

Overall, 63% correctly comprehended the text-only condition and 60% comprehended the line graph condition, compared with 83% for the visual analogy and 70% for the number line (P = .05) conditions. Participants comprehended the visual analogy significantly better than the text-only (P = .02) and line graph (P = .02) conditions. Of participants who comprehended at least 1 condition, 14% preferred a condition that they did not comprehend. Low comprehension was associated with worse cognition (P < .001), lower education level (P = .02), and fewer financial resources (P = .03).

CONCLUSIONS

The results support using visual analogies rather than text to display longitudinal PROs but caution against relying on graphs, which is consistent with the known high prevalence of inadequate graph literacy. The discrepancies between comprehension and preferences suggest factors other than comprehension influence preferences, and that future researchers should assess comprehension rather than preferences to guide presentation decisions.

Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts

BACKGROUND

Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids.

METHODS

As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version.

RESULTS

Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates.

DISCUSSION

More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.

Visualización mediática de la ciencia: tipología de la infografía científica de prensa

Este artículo explora el tratamiento informativo de la infografía de la prensa internacional para emprender de manera eficaz procesos de comunicación y divulgación de los hechos relativos a la Ciencia y Tecnología (CyT). La integración de un método de análisis de contenido, aplicado a una muestra seleccionada de mejores prácticas en materia de infografía de periódicos y revistas que han recibido reconocimiento internacional a través de los premios Malofiej (N=149), fueron codificadas y analizadas para identificar características y patrones detallados con base a tres ejes de estudio: el planteamiento informativo (¿qué se dice?), los objetos gráficos y su correspondencia (¿cómo se dice?) y la función que desempeñan los infográficos (¿para qué se dice?). Los resultados indican que estas piezas informativas emplean distintos planteamientos informativos que reflejan en sus mensajes, los cuales conllevan al uso particular de determinados recursos de expresión (objetos gráficos), desempeñando de manera conjunta, diversas funciones comunicativas por parte del emisor con respecto al público al que se dirige; lo anterior, llevó a la conformación y descripción de tres modelos de infografía científica de prensa.

Probability Size Matters: The Effect of Foreground-Only versus Foreground+Background Graphs on Risk Aversion Diminishes with Larger Probabilities

Graphs are increasingly recommended for improving decision-making and promoting risk-avoidant behaviors. Graphs that depict only the number of people affected by a risk ("foreground-only" displays) tend to increase perceived risk and risk aversion (e.g., willingness to get vaccinated), as compared to graphs that also depict the number of people at risk for harm ("foreground+background" displays). However, previous research examining these "foreground-only effects" has focused on relatively low-probability risks (<10%), limiting generalizability to communications about larger risks. In two experiments, we systematically investigated the moderating role of probability size on foreground-only effects, using a wide range of probability sizes (from 0.1% to 40%). Additionally, we examined the moderating role of the size of the risk reduction, that is, the extent to which a protective behavior reduces the risk. Across both experiments, foreground-only effects on perceived risk and risk aversion were weaker for larger probabilities. Experiment 2 also revealed that foreground-only effects were weaker for smaller risk reductions, while foreground-only displays decreased understanding of absolute risk magnitudes independently of probability size. These findings suggest that the greater effectiveness of foreground-only versus foreground+background displays for increasing perceived risk and risk aversion diminishes with larger probability sizes and smaller risk reductions. Moreover, if the goal is to promote understanding of absolute risk magnitudes, foreground+background displays should be used rather than foreground-only displays regardless of probability size. Our findings also help to refine and extend existing theoretical accounts of foreground-only effects to situations involving a wide range of probability sizes.

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

Probabilistic seismic risk analysis is a well-established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real-world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.

Assessing the Effects of Information About Global Population Growth on Risk Perceptions and Support for Mitigation and Prevention Strategies

The human population is forecast to increase by 3-4 billion people during this century and many scientists have expressed concerns that this could increase the likelihood of certain adverse events (e.g., climate change and resource shortages). Recent research shows that these concerns are mirrored in public risk perceptions and that these perceptions correlate with a willingness to adopt mitigation behaviors (e.g., reduce resource consumption) and preventative actions (e.g., support actions to limit growth). However, little research has assessed the factors that influence risk perceptions of global population growth (GPG). To contribute to this important goal, this article presents three studies that examined how risk perceptions of GPG might be influenced by textual-visual representations (like those in media and Internet articles) of the potential effects of GPG. Study 1 found that a textual narrative that highlighted the potential negative (cf. positive) consequences of GPG led to higher perceived risk and greater willingness to adopt mitigation behaviors, but not to support preventative actions. Notably, the influence of the narratives on perceived risk was largely moderated by the participant's prior knowledge and perceptions of GPG. Contrary to expectations, studies 2 and 3 revealed, respectively, that photographs depicting GPG-related imagery and graphs depicting GPG rates had no significant effect on the perceived risk of GPG or the willingness to embrace mitigation or preventative actions. However, study 3 found that individuals with higher "graph literacy" perceived GPG as a higher risk and were more willing to adopt mitigation behaviors and support preventative actions.

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk-avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm ("foreground+background") versus only those affected ("foreground-only"), and the presence versus absence of simple numerical labels above bars. Foreground-only displays were associated with larger risk perceptions and risk-avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground-only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground-only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk-avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk-avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground-only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.

Biasing and debiasing health decisions with bar graphs: Costs and benefits of graph literacy

Bar graphs can improve risk communication in medicine and health. Unfortunately, recent research has revealed that bar graphs are associated with a robust bias that can lead to systematic judgement and decision-making errors. When people view bar graphs representing means, they tend to believe that data points located within bars are more likely to be part of the underlying distributions than equidistant points outside bars. In three experiments, we investigated potential consequences, key cognitive mechanisms, and generalisability of the within-the-bar bias in the medical domain. We also investigated the effectiveness of different interventions to reduce the effect of this bias and protect people from errors. Results revealed that the within-the-bar bias systematically affected participants’ judgements and decisions concerning treatments for controlling blood glucose, as well as their interpretations of ecological graphs designed to guide health policy decisions. Interestingly, individuals with higher graph literacy showed the largest biases. However, the use of dot plots to replace bars improved the accuracy of interpretations. Perceptual mechanisms underlying the within-the-bar bias and prescriptive implications for graph design are discussed.