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As the year 2023 draws to a close, many people (especially here in North America) will remember it as a smoky one. Although regions across the western US and Canada have contended with smoke from wildfires for some time, 2023 saw this health hazard blanket communities who had rarely—if ever—experienced such dangerous levels of air pollution before. While remarkable images of city skylines painted orange and national landmarks surrounded by dense haze quickly made headlines around the world, about 70 million people suddenly found themselves under air quality alerts from the wildfire smoke. They were told to protect themselves and reduce their smoke exposure because breathing in particulate matter and other harmful compounds found in wildfire smoke can exacerbate many health conditions like asthma or heart disease, among other health effects. But the health impacts from wildfire smoke especially affect sensitive groups like children, who tend to experience higher rates of hospitalization for respiratory issues (e.g., wheezing, asthma, infections) following exposure to wildfire smoke. With wildfire smoke events set to increase in frequency and intensity due to climate change, more parents in more places will need to know how to protect their children from exposure. As trusted messengers, healthcare professionals are well positioned to provide advice to parents about the potential health impacts of wildfire smoke and how to minimize harm among children. So we teamed up with Dr. Rebecca Philipsborn, a pediatrician at Children’s Healthcare of Atlanta and an Associate Professor at Emory University’s School of Medicine, to put together strategies for healthcare professionals to communicate to families about wildfire smoke and children’s health risks. Based on foundational principles from the behavioral sciences, we recommended three strategies healthcare professionals could use to increase parents’ understanding of wildfire-smoke risks and promote actions to keep kids safe; we recently published this work in BMJ Paediatrics Open. The strategies were: 1. USE VISUALS AND STORIES TO MOTIVATE THE USE OF AIR QUALITY INDICES. Since visuals effectively capture people’s attention, they can serve as powerful communication tools to motivate the adoption of smoke-safe behaviors like checking daily air quality levels during wildfire smoke events. An Air Quality Index (AQI) is a numeric scale used to inform people how clean or polluted the air near them is, often using a simple color-coded visual to communicate that information. We recommend that healthcare professionals become familiar with the AQI used in their jurisdiction and point parents toward these visuals, available on government websites and through smartphone apps (e.g., weather apps). High AQI values correspond to higher levels of health risk, and healthcare professionals can help guide parents’ decision making about what actions to take (discussed in Strategies #2 and 3 below) when the AQI reaches harmful levels. When communicating about wildfire smoke, clinicians should draw on their personal experiences using the AQI as a tool to decide what activities are safe and use stories to make examples of actions more concrete, real, and personally relevant to families. 2. EMPHASISE NEAR-TERM HEALTH BENEFITS OF REDUCED EXPOSURE TO WILDLIFE SMOKE. The best way to eliminate children’s exposures to smoke is to relocate to areas with better air quality. However, this option can be impractical, especially during long-term smoke events. Still, we believe that healthcare professionals can recommend other alternatives. For example, time spent outdoors should be reduced, and outdoor events can be rescheduled or relocated whenever possible. Masking is another option. However, clinicians should guide parents on how to use well-fitted N95 respirator masks since medical and cloth masks provide limited protection from wildfire smoke. When discussing these options, healthcare professionals should emphasize how actions will benefit children’s health, for example, by reducing respiratory symptoms like cough and wheezing, as discussing specific risks and solutions can help parents understand smoke harms and motivate protective actions. 3. IDENTIFY AND REDUCE PATIENT BARRIERS TO ACCESSING CLEAN INDOOR AIR SPACES. Much of the guidance around protecting children from wildfire smoke directs parents to keep their children indoors; thus, ensuring indoor spaces are safe is extremely important. Healthcare professionals should advise families about ways to improve the quality of indoor air at home or where they can access clean air spaces in their community. While ventilation and air purification systems using high efficiency particulate air filters are highly effective at reducing people’s exposure to wildfire smoke indoors, they can also be costly. As a result, healthcare professionals should become familiar with low-cost alternatives (e.g., box-fan systems) and any local programs that provide people access to clean air spaces. We also recommend for healthcare professionals to become advocates for and champions of healthy air quality policies. The medical community has a strong voice that can shape guidelines and standards for indoor air quality or influence what actions schools take when AQIs reach a certain level during smoke seasons. To learn more about these strategies, check out our piece here! This work was supported in part by The Banting Postdoctoral Fellowships program of Canada and the US National Science Foundation (SES-2017651).

How numerate are you? See if you can answer the following question: If 50 people in a town of 100,000 people catch a virus, what percent of the town has the virus? ______% of the town (answer at the end of the blog) *Please note that this question is not part of any of the measures but was calibrated alongside the items in the Numeric Understanding Measures.* WHAT IS NUMERACY? Numeracy refers to a person’s ability to understand and use numbers, including basic probabilistic and mathematical concepts. It is often thought of as numeric literacy. Numeracy is important because it is associated with financial and health outcomes. Highly numerate people tend to make more money and are more likely to be employed compared to the less numerate. The less numerate are also more likely to have a chronic disease and take more prescription drugs, all while having less ability to follow complex health regimens. Innumeracy is a problem in the US, especially as data becomes more and more accessible. In fact, about 30% of US adults can only perform simple processes with numbers—counting, sorting, simple arithmetic, simple percents—and they can only do so if there is little text and minimal distractors around the numbers. MEASURING NUMERACY Numeracy is assessed using a math test. However, existing numeracy measures have flaws. Some are too easy, making them less discerning for the more highly numerate. Others are too difficult, so you cannot distinguish among people with lower skills. Still others have been used and publicized, and their answers can be easily found online. To solve these issues, we created 84 new math problems and calibrated them using item response theory (IRT)—a method of modeling how latent traits are related to responses on items. IRT tells us how informative an item is and at what level of numeracy it is most informative. Using this information, we created three short new Numeric Understanding Measures (NUMs). The A-NUM is adaptive, meaning it asks different questions based on the participant’s performance and requires the participants to answer four questions. The 4-NUM measure also has four items, but all participants see the same four items. The single-item measure uses one of the items from the adaptive measure. VALIDATING NUMERACY MEASURES To ensure that we measure what we mean to measure, we validated the new measures. Participants completed our new numeracy measures and two established numeracy measures to test whether all the numeracy measures measured the same thing. We used a confirmatory factor analysis and concluded that they did. Latent numeracy explains about 74% of the variability on A-NUM scores, 70% of the variability in 4-NUM scores, 65% in Weller scores, and 56% in Berlin scores. Next, we tested if the measure was correlated with similar constructs and less correlated with dissimilar constructs; thus, we tested convergent and discriminant validity, respectively. Think about convergent and discriminant validity as a continuum. Some variables should be highly correlated with the NUMs, like other measures of numeracy. Other variables might be fairly highly correlated, like subjective numeracy and its two subscales of numeric self-efficacy and numeric preference. We expected a non-verbal measure of fluid intelligence (Raven’s matrices) and a measure of crystallized intelligence (vocabulary) to have weaker correlations; Big 5 personality traits should be weakly or not at all related. Our new NUMs correlated as expected and demonstrated similar correlations to these variables as more established measures of numeracy. Lastly, we checked how much the NUMs predicted behaviors in tasks previously related to more established numeracy measures (predictive validity). We expected that more numerate individuals would more accurately interpret probabilities, better identify numeric information needed to interpret a product's benefit, show weaker framing effects, rate an inferior bet with a small loss as more attractive, and have more consistent risk perceptions. Overall, the new measures predicted behaviors for all the tasks that established measures also predicted. No measure, however, predicted framing effects, an unexpected finding. The NUMs were created to measure a wide range of numeric ability using only a few items. We demonstrated convergent, discriminant, and predictive validity. Moreover, the new measures appear to assess numeracy in a more fine-grained manner as compared to established measures. Importantly, these new measures are comprised of items not easily found online. For more information about the development and validation of the NUMs, please see the article published in Judgment and Decision Making: Silverstein, M.,Bjälkebring, P., Shoots-Reinhard, B., & Peters, E. (2023). The numeric understanding measures: Developing and validating adaptive and nonadaptive numeracy scales. Judgment and Decision Making, 18, E19. https://doi.org/10.1017/jdm.2023.17 For information about how to use the NUMs in your research (including the items and Qualtrics coding to present and score them), please see caidelab.com/numeracy. This work was supported by the National Science Foundation (Grant No. 2017651) and the Decision Sciences Collaborative at The Ohio State University. ANSWER: 0.05% of the town.

Communicators sometimes think that the public cannot “handle” numbers. However, when provided them, people can understand more and act in healthier ways. For example, providing numeric likelihoods of a medication’s side effects increased risk understanding and willingness to take it. Similar positive numeric effects emerged in vaccines, cancer screening, and treatment advertising. Further, people trust and engage more with messages that include numbers. How numbers are formatted though can matter as much as what numbers are presented. In this project, eight studies were conducted in order to develop a set of concrete recommendations about data visualizations. The recommendations include tested guardrails (sets of do’s and don’ts for visualizing data). We recruited 1,648 online participants divided about evenly between younger adults (25-34 years), middle-aged adults (35-54 years), and older adults (55+); we further recruited some participants aged 18-24 years. All participants completed baseline measures in an initial session and then responded to eight experiments divided into two subsequent sessions. In each experiment, participants were given graphical information or a no-graphic control condition and answered questions assessing comprehension of the numeric information. For example, in Experiment 1, some participants saw a graph like the one below. In other conditions, instead of authorized immigrants, participants were shown the total number of immigrants. In the no-graphic condition, they only saw the text. For other participants, the graphs were included but the text was omitted. Participants were asked questions such as “True or false: The number of unauthorized immigrants has increased more than the number of authorized immigrants over the past three decades.” From these data, we developed overall recommendations for people of moderate numeracy (i.e., those that answer two out of four numeracy questions correctly). We summarize three of those recommendations here. 1. Provide data visuals. In three experiments, we included control conditions where graphical information was not provided—for immigration, air quality, and COVID-19 vaccination rates, respectively. In all three cases, comprehension was higher when graphics were provided vs. not. 2. Provide less information to increase comprehension. We sometimes gave participants cues—such as color, bubble size, labels, and trend-lines. Providing cues helped comprehension of questions related to their content (i.e., size of changes in wages) but hurt comprehension of questions unrelated to their content (i.e., current wages). For example, participants viewed figures depicting changes in wages by industries over a 15-year period. Figures showed industries ordered by the highest wage in 2019 or the size of the change over time. To half of the figures, we added category labels indicating whether the change was a “large increase,” “small increase” or “decrease.” These labels helped participants identify the industry with the largest decline (below, left), but made it more difficult for them to identify the industry with the highest wage (below, right). 3. Provide information in a format that matches the communication goal. The format of information often increased understanding of one aspect of the data while hurting comprehension of another aspect. For example, all participants viewed graphs with the number of authorized immigrants and either the number of unauthorized immigrants or the number of total foreign-born. When it was graphed with total foreign-born people in the US, participants understood the total better (green bar on the left below) and understood less about the number of unauthorized immigrants (green bar on the right below). The opposite occurred when we graphed the number of authorized immigrants and unauthorized immigrants (gray bars below). In other words, graphing two categories (authorized and unauthorized immigrants) helped comprehension of the categories, but hurt comprehension of the total (relative to graphing one category and the total). For more on how to present numbers so that they matter, check out Innumeracy in the Wild: Misunderstanding and Misusing Numbers (Oxford University Press). “Policies in health and financial domains have shifted toward giving consumers and patients more information (which is often numeric). These changes are intended to empower individuals to take charge of their own welfare. The evidence is clear, however, that not everybody is prepared to use this information effectively and that those who are less numerate tend to make worse decisions unless provided adequate support. The book discusses … methods that exploit existing evidence and enable those who are less comfortable with numbers to use them more effectively and make better choices in an often numeric world.” This work was supported by grants provided by the National Science Foundation (SES- 2017651) and USAFacts. The content of this post is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or USAFacts.

Shoots-Reinhard, B., Lawrence, E. R., Schulkin, J., & Peters, E. (2022). Excluding numeric side-effect information produces lower vaccine intentions. Vaccine. doi: https://doi.org/10.1016/j.vaccine.2022.06.001 If you use the following link before July 30, 2022, you can read a copy of the article for free: https://authors.elsevier.com/a/1fDkP,60n7kMYm In a public health crisis like COVID-19, vaccine uptake can have striking effects on disease outcomes. As one U.S. example, during the summer of 2021, unvaccinated Americans died at 11 times the rate of vaccinated Americans. Despite these striking statistics, many Americans remain concerned about vaccine safety. Despite this concern, communication about side effects does not often inform people about how likely the side effects are. However, research from our lab and others has demonstrated that providing likelihoods for medications (e.g., “10%)—instead of verbal likelihood labels (e.g., “common”)—reduces overestimation of side effect likelihood and increases medication uptake. We suspected similar effects may occur with vaccines. In a diverse online convenience sample (N = 595), we told people to imagine their doctor recommended a vaccine and gave them a list of side effects of the hypothetical vaccine. Some participants only saw the list of side effects (right column below), some saw the list + verbal likelihood labels (right two columns), some saw the list + numeric likelihood information (left and right columns), and some saw the list with both verbal and numeric information (all three columns): As we suspected, providing numeric information increased vaccine intentions—70% of those who received numeric information were predicted to be moderately or extremely likely to vaccinate compared to only 54% of those who did not receive numeric information (p<.001), controlling for age, gender, race, education, and political ideology. We found even more striking effects for people who indicated in an earlier survey that they were hesitant to get a vaccine a doctor recommended. For these hesitant participants, there appeared to be a benefit to giving both numeric and verbal risk information (Figure 1). Among the vaccine hesitant, 43% were predicted to be moderately or extremely likely to get the vaccine when provided numeric information and verbal labels compared to only 24% in the list only (i.e., standard-of-care) group, controlling for covariates. Figure 1. Vaccine-hesitant participants (on the right) were more willing to receive the recommended vaccine when provide numeric and verbal likelihood information (scale: 1=not likely to get vaccine to 6=extremely likely to get vaccine). Additional analyses suggested that: 1) the numeric information reduced the number of side effect likelihoods that were overestimated and 2) the combination of numeric and verbal likelihood information helped people realize that most side effects were not serious; it also reduced concern about rare, very serious blood clotting. We suspect that a switch from the standard list of side effects to more detailed information including both numeric information and verbal labels could help increase COVID-19 vaccination rates. For example, in October 2020, about 120 million people were hesitant to vaccinate. If we assume an effect on actual vaccination half the size we found on intentions, then we estimate that about 11 million more Americans (3% more of the population) would have been convinced to vaccinate. This project was supported by the United States National Science Foundation (SES-2022478, SES-2017651, and SES-2029857), United States Health Resources and Services Administration (UA6MC19010), and a First Year Research Experience Grant from the University of Oregon.

In today’s blog post, we will discuss a paper recently published online first in the Scholarship of Teaching and Learning in Psychology examining the effects of disruption due to the COVID-19 pandemic and math ability on undergraduate students’ grades and interest in taking additional math courses. Svensson, H., Shoots-Reinhard, B., Cravens-Brown, L., & Peters, E. (2022). Greater objective numeracy protects COVID-19 pandemic grades but endangers academic interest. Scholarship of Teaching and Learning in Psychology. Advance online publication. http://dx.doi.org/10.1037/stl0000319 You can download the paper for free by using this link. Prior research has consistently highlighted the benefits of being good at math (i.e., being high in objective numeracy) on academic and broader life-related outcomes, like having better control of chronic medical conditions and improved financial decision-making. In the current study, we were interested in whether being good at math would protect undergraduate students against the potentially negative effects of the COVID-19 pandemic. In a study of 399 undergraduate students at The Ohio State University enrolled in an introductory-level data analysis course, we explored the impact of the pandemic on students who varied in their math abilities. We expected that being better at math would protect students from the potentially harmful effects of the COVID-19 pandemic. Indeed, students’ course grades during the pandemic were protected by math ability. No differences existed in course grades for high-ability students who experienced more vs. less pandemic-related disruption. However, this was not the case for low-ability students. Low-ability students had worse grades when they experienced more (vs. less) disruption (Figure 1). Figure 1. Objective numeracy and pandemic-related disruption predict grades during the pandemic Note: Participants’ grades during the pandemic are reported on a range from 0% to 100%. However, and of surprise to us, we found the opposite when we examined how math ability and pandemic-related disruption affected students’ interest in taking future math courses. Students high in math ability who also experienced more pandemic-related disruption were less likely to indicate interest in taking additional math courses than high-ability students experiencing less disruption. In fact, by using their transcripts, we discovered that about 30% fewer high-ability students enrolled in an advanced statistics course when they experienced high (vs. low) levels of pandemic-related disruption. For those low in math ability, disruption did not affect interest in taking future math courses (Figure 2). Figure 2. Objective numeracy and pandemic-related disruption predict future math interest Participants answered three questions about their interest in taking additional math courses. Math interest was coded such that 1 indicated the lowest likelihood of taking additional math courses (i.e., “very unlikely”) and 6 was the highest likelihood of taking additional math courses (i.e., “very likely”). These findings suggest that students higher in math ability may perform better—but paradoxically feel worse—when faced with unexpected hardships. As a result, educators need to be mindful of their high-ability students’ struggles because their academic motivation may be less resilient than previously expected and especially during unique times like the pandemic. This research was supported by a Student Academic Success Research Grant from The Ohio State University and the National Science Foundation. Key words: objective numeracy, numeric self-efficacy, academic outcomes, COVID-19, higher education

Today’s blog post focuses on a new paper, just published in PLOS ONE. In it, we ask whether numeracy seems to make a difference to life satisfaction (and how). The paper is open source and can be downloaded for free by using this link. Bjälkebring, P., & Peters, E. (2021). Money matters (especially if you are good at math): Numeracy, verbal intelligence, education, and income in satisfaction judgments. PLOS ONE, 16(11), e0259331. https://doi.org/10.1371/journal.pone.0259331. Using a large, diverse sample of Americans (N= 5,748), we demonstrated that people better at math made more money. For every one additional question they answered correctly on an eight-item math test, they reported $4,062 more in annual income. People better at math also were more satisfied with their lives than those with lower math ability, suggesting—perhaps not surprisingly—that income matters to life satisfaction. However, we also wanted to think about the idea that numeracy might change how people looked at the world around them. We thought that the relation between numeracy and life satisfaction might not be as simple as greater numeracy = higher income and higher life satisfaction. Research on income and life satisfaction suggests that how satisfied we are with our income depends on how we feel it compares to other people’s incomes. For example, if I make $50,000 a year, I might be very satisfied, but if someone tells me that a newly hired colleague makes $60,000 for the same work, I will likely feel less satisfied. Hence, how I feel about my salary will depend on how it compares to others’ incomes. These are called relative-income effects. But people better at math tend to compare numbers more than those worse at math. Might they compare incomes more? Consistent with this, life satisfaction among people better at math related much more to their incomes; they were happier if their income was higher. For people worse at math, their life evaluations depended much less on income. As a result, people better at math had the highest life satisfaction (when they had high incomes) and the lowest life satisfaction (when they had lower incomes). Thus, the same income was valued differently by those better and worse at math. See Figure 1. Figure 1. The people that scored 8 questions correct on our math test both had the highest and the lowest life satisfaction depending on their income. Finally, those better at math were happier with every income increment; no clear income satiation point was seen among them. For people worse at math, however, they were happier with more income only up until about $50,000, after which earning more made little difference. Those worse at math may derive their life satisfaction from sources other than income. See Figure 2. Figure 2. People that scored highest on our math test (red line) was more influenced by their income and their line was steeper compare to those who scored lower, meaning they were influenced more. You can also see that at points lower than the average income (to the left of the arrow) the red line is on the bottom, but at higher incomes (to the right of the arrow) the red line is on top indicating that math ability is most beneficial for those with higher than average incomes. More detail about our methods: Participants were panel members of the Understanding America Study, which is maintained by the University of Southern California (USC). An address-based sampling method was used to recruit participants. Panel members have completed modules over time and were paid accordingly (e.g., $5 for a 5-minute module). Only panelists who completed all relevant measures were included in analyses, resulting in a final N= 5,748 in our sample. Participants answered an eight-question math test, as well as questions about their income, income satisfaction, and life satisfaction. In analyses, we controlled for verbal intelligence and education and their interactions with income, as well as personality traits and other demographics. Lastly, we would like to thank our funders, the Swedish Research Council (VR; DNR-2016-00507) to P. B. and the National Science Foundation (SES‐1155924, SES‐1558230 and SES-2017651) to E. P. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of our funders, USC, or UAS. The paper is open source so you can download the paper for free by using this link. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Meaning that you can reuse the text, figures and graphs on your blog, journal newspaper, book etc., as long as original authors and source are credited.

Tiede, K. E., Bjälkebring, P., & Peters, E. (2021). Numeracy, numeric attention, and number use in judgment and choice. Journal of Behavioral Decision Making. Advance online publication. https://doi.org/10.1002/BDM.2264 In our new paper, we found that people who score higher in numeracy, the ability to understand and use probabilities and numbers, look more often at numeric information when making judgments and decisions than those who are lower in numeracy. In three studies, we provided participants with information about consumer products (e.g., a dishwasher). The information was either only numeric (e.g., 77 out of 100 points) or included both numbers and descriptive labels (e.g., “good”) (see Figure 1). Participants were then asked to rate the product’s attractiveness or to choose between two kinds of the same product. In all studies, we measured the information at which participants looked and for how long they looked. Figure 1. Screenshot of the choice task in Studies 2a and 2b: Participants had to hover their mouse cursor over a box to see the covered information. The respective label for the 71-point rating was “good”. Across all studies, there were three key findings: We found that people higher in numeracy looked more often (but not longer) at numeric information than those with lower numeracy when combining the data of all three studies. We replicated previous findings that people higher in numeracy relied more strongly on numeric information than their less numerate counterparts. Importantly, numeric attention could statistically explain the relationship between numeracy and number use in one of the two studies that tested this relation. Specifically, people higher (vs. lower) in numeracy looked more often at numbers. This increased numeric attention, in turn, was related to an increased use of numeric information. Finally, our studies also demonstrated that numeric attention is driven specifically by numeric abilities rather than general intelligence or the subjective preference for numbers (i.e., subjective numeracy). Our results help to understand how numeracy affects judgment and decision making and to design decision aids to improve the decisions of people both lower and higher in numeracy. To learn more about numeracy and decision making, check out: Peters, E. (2020). Innumeracy in the wild: Misunderstanding and misusing numbers. Oxford University Press. Named a New Books Network podcast book of the day, https://newbooksnetwork.com/innumeracy-in-the-wild and featured by Oxford University Press as on the frontiers of psychology research.

Today’s blog is Part 3 of a 3-part series explaining some of the background and results for a paper recently accepted in the journal Intelligence. In it, we examine ability-based polarization in COVID-19 and in non-COVID topics. (Click for Part 1 and Part 2) Shoots-Reinhard, B., Goodwin, R., Bjälkebring, P., Markowitz, D., Silverstein, M.D., & Peters, E. (forthcoming). Ability-related political polarization in the COVID-19 pandemic. Intelligence. https://doi.org/10.1016/j.intell.2021.101580 You can download the paper for free before October 9, 2021 by using this link. In the UO-EPIDeMIC study, we found that political polarization in reported negative emotions and risk perceptions was more pronounced among those higher vs. lower in verbal ability. After controlling for it, numeracy-related political polarization disappeared. We were interested in what types of processes might contribute to this polarization. In several survey waves in 2020, we asked participants to indicate how much they get information about the coronavirus from four news sources that liberals trust (i.e., New York Times, MSNBC, Washington Post, and NPR) and two sources that conservatives trust (i.e., Fox News and Breitbart). As expected, people spent a greater proportion of time on sources that matched their political ideology. We tend to read and listen to people with whom we agree. This effect is the well-known selective exposure to information that can underlie motivated thinking. People higher in verbal ability did it more! This greater political polarization in media consumption by those higher in verbal ability might underlie why higher-ability people became more polarized. By exposing themselves to different knowledge, they can might use it in turn to justify what they want to believe, their polarized beliefs and reactions. But selective exposure to information isn’t the only possible reason for polarization. We were also interested in what would happen if people had access to the same information and had to interpret it. We thought that in addition to selectively exposing themselves to different information, people higher in ability might also interpret the same information to confirm their political views. For example, we asked people whether or not they agreed that “If there are only a very few cases of coronavirus appearing in my community now, the risk in the future can be considered to be small.” Conservatives were more likely to agree than liberals, and especially when they were higher in ability. These interpretations also may have contributed to the polarized responses to coronavirus, just as polarized media consumption did. We found the same pattern when we asked people to interpret lower-than-expected COVID-19 deaths. Here, conservatives were more likely than liberals to attribute the reduction to the CDC overstating risks rather than social distancing reducing the risks. Similar results emerged in people’s confidence that they could go back to normal if they had a positive COVID antibody test (liberals were less confident than conservatives). In all cases, polarization was more pronounced for people higher in verbal ability, and the polarized interpretations accounted for the polarized emotional responses and risk perceptions. To summarize, people higher in verbal ability both selectively consume news and interpret information in ways consistent with their political ideology. These political polarized ways of consuming and processing information then seem to contribute to their polarized beliefs. The results point towards the notion that we can’t reduce political polarization simply by providing more information—people will avoid information they disagree with and interpret evidence to be consistent with their existing views. Instead, we need to investigate ways to encourage people higher in ability (and verbal ability, in particular) to accept information that conflicts with their views. This research was supported by grants from the National Science Foundation (SES-20010000 and SES-2017651). Portions of these data will be published in the journal Intelligence (https://doi.org/10.1016/j.intell.2021.101580).You can download the paper for free before October 9, 2021 by using this link.

Today’s blog is Part 2 of a 3-part series explaining some of the background and results for a paper recently accepted in the journal Intelligence. In it, we examine ability-based polarization in COVID-19 and in non-COVID topics. (Click for Part 1) Shoots-Reinhard, B., Goodwin, R., Bjälkebring, P., Markowitz, D., Silverstein, M.D., & Peters, E. (forthcoming). Ability-related political polarization in the COVID-19 pandemic. Intelligence. https://doi.org/10.1016/j.intell.2021.101580 You can download the paper for free before October 9, 2021 by using this link. Participants in the UO-EPIDeMIC study completed measures of numeracy (numeracy is like literacy, but with math ability) and verbal reasoning in February 2020. We were interested in how these abilities might influence perceptions of COVID-19. It’s intuitive to think that people higher in math and verbal ability would be less biased— but people can also use cognitive ability to support existing viewpoints and resist contrary evidence. For example, people who are more knowledgeable about politics often have more polarized political attitudes. Indeed, we found that people higher in verbal ability were more polarized in their emotional responses and risk perceptions concerning COVID-19. In other words, the differences we found between conservatives and liberals were greater for those who did better on our test of verbal ability (see below, left panel). People who were higher in numeracy reported lower emotional reactions and risk perceptions than people lower in numeracy, but people higher and lower in numeracy were similarly polarized (see below, right panel). Predicted risk perceptions by political ideology among people scoring in the top and bottom thirds of verbal ability (left) and numeracy (right) Participants answered six questions about their risk perceptions to COVID-19. Risk perceptions were coded so that 1 was the minimum risk perception (e.g., “no risk”) and 6 was the maximum risk perception (e.g., “extreme risk”) This finding surprised us because we had anticipated that numeracy would be related to polarization, as others have found (although these effects don’t always replicate fully). Instead, we consistently found that verbal ability was associated more with polarization than was numeracy. We reasoned that, because ability variables are correlated, earlier motivated numeracy effects (including our own results) might have been driven by correlations between numeracy and verbal ability. Indeed, we found in a separate publicly available dataset, the Understanding America Study, that numeracy predicted greater polarization, as in the prior research—but only when verbal abilities weren’t included in the same predictive model. When we pitted numeric and verbal ability, we found only verbal ability, not numeracy, predicted greater polarization. This pattern of results is further evidence that motivated numeracy effects may in fact be motivated verbal ability effects. These findings suggest that polarization could be a function of the knowledge people have and their ability to form arguments in support of their political views. We examined two potential ways in which ability could lead to polarization in our study, and we will discuss them in a future post. This research was supported by grants from the National Science Foundation (SES-20010000 and SES-2017651). Portions of these data will be published in the journal Intelligence (https://doi.org/10.1016/j.intell.2021.101580). You can download the paper for free before October 9, 2021 by using this link.

Setting aside the differences between the strategies we asked about in our online sample of American drivers (N=648), drivers that supported the strategies more overall had greater anti-CUWD beliefs, perceived CUWD as more risky, and reported lower negative reactions to warnings against distracted driving. People who felt worse about warnings were also more likely to report that they drive distracted more often. Thus, the most distracted drivers are also going to be the least supportive of strategies to reduce distracted driving and the most difficult to educate about the risks of distracted driving. We were also curious about whether and who might react poorly to the messages. Reactance is a combination of negative emotions to messages (e.g., annoyance or anger with messaging) and beliefs that the messages are manipulative and risks are exaggerated or overblown. We’ve found in this and other surveys of drivers that reactance is one of the strongest predictors of dangerous driving behavior. Fortunately, reactance in our study was unrelated to support for hands-free use of cellphones while driving. One way to sidestep resistance to messaging and overcome the challenges of convincing people CUWD is risky is to tell them they can still use their phone—just use it hands-free. Large-scale studies of drivers suggest that hands-free use of phones to hold conversations is less risky than manually using phones while driving (e.g., to dial, text, or scroll). If drivers who currently manually use their phones switch to hands-free, it should reduce the number of crashes. States that have hands free laws have fewer fatal crashes. Because cellphone use is more common and more dangerous in younger drivers, and younger drivers are more reactant to anti-distraction messaging , these laws have particular potential to protect young drivers. This research was supported by grants from The Risk Institute at The Ohio State University, Ohio Department of Transportation, and the National Science Foundation (SES-1558230), and will be published in Traffic Injury Prevention (https://doi.org/10.1080/15389588.2021.1964076).

Today’s blog is Part 1 of a 3-part series explaining some of the background and results for a paper recently accepted in the journal Intelligence. In it, we examine ability-based polarization in COVID-19 and in non-COVID topics. Shoots-Reinhard, B., Goodwin, R., Bjälkebring, P., Markowitz, D. M., Silverstein, M. C., & Peters, E. (2021). Ability-related political polarization in the COVID-19 pandemic. Intelligence, 88, 101580. doi: https://doi.org/10.1016/j.intell.2021.101580 You can download the paper for free before October 9, 2021 by using this link In the UO-EPIDeMIC study (our longitudinal examination of COVID-19 reactions), we found that people’s personal risk perceptions and emotions appeared to track together over the year 2020—when emotions were the most negative, people thought they were more likely to get COVID-19 and were also more likely to look at statistics daily. In addition to tracking responses over time, we also examined the role of political ideology on people’s reactions over time. As shown below, conservatives and liberals initially (February 17-25, 2020) did not differ much in terms of negative emotions. Negative emotions then increased over time for both groups, and an ideological difference also emerged and persisted. Conservatives reported lower negative emotions than liberals. We asked participants to indicate “How does the coronavirus make you feel?” on a scale from 1 = “does not apply/describe” to 4 = “completely describes” for afraid, worried, and upset. Similarly, people’s risk perceptions showed political polarization starting in March 2020—people identifying as liberal (vs. conservative) perceived the coronavirus as riskier over time. Participants answered six questions about their risk perceptions to COVID-19. Risk perceptions are coded so that 1 is the minimum risk perception (e.g., “no risk”) and 6 is the maximum risk perception (e.g., “extreme risk”) This research was supported by grants from the National Science Foundation (SES-20010000 and SES-2017651). Portions of these data will be published in the journal Intelligence (https://doi.org/10.1016/j.intell.2021.101580). You can download the paper for free before October 9, 2021 by using this link.

In an online sample of American drivers (N=648), we investigated the influence of language on support for legislative, technological, and organizational strategies for reducing cellphone use while driving (CUWD). We found that support varied across strategies—less restrictive strategies were supported more than more restrictive strategies. For example, 87% of the sample supported school or workplace pledges to not drive distracted whereas 80% supported school or workplace bans on CUWD. But the language used to describe the restriction matters. The same strategy was supported more when it was described using less restrictive language. Apps and technology that “help you drive without using your phone” were preferred to those that “prevent you from using your phone.” See the figure below. Similarly, laws that monetarily fine people who use phones while driving were preferred to “bans” on using phones (although “bans” are just another way of describing a law that punishes CUWD with fines). Finally, insurance programs that charge good drivers less are preferred to those that charge poor drivers more. Our findings highlight that the language used by policy makers and other stakeholders will influence the public’s support for a strategy. Insurance companies also will have better luck attracting customers if they talk about safe driving discounts rather than penalties for dangerous drivers, and app developers should frame their technology as helping people avoid dangerous behavior rather than preventing them from engaging in dangerous behavior. Finally, support for enforcement of cellphone use will be undermined if laws are described as bans. This research was supported by grants from The Risk Institute at The Ohio State University, Ohio Department of Transportation, and the National Science Foundation (SES-1558230), and will be published in Traffic Injury Prevention (https://doi.org/10.1080/15389588.2021.1964076).