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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).

In March, people were most likely to look up COVID statistics every single day. Negative emotions to COVID-19 peaked that same month. From May-December, emotions and looking at statistics every day then both remained fairly steady. It is still unclear whether negative emotions led to seeking out statistics or whether seeking out statistics led to negative emotions, as discussed in this New York Times piece. Scientists and clinicians can do their part to present statistics in ways that improve understanding, and consumers can also seek out information to help them use statistics more appropriately.

We asked our participants to predict the numbers of infections in 2020 six times from February to December and to predict the number of deaths in 2020 five times from March to December. Numeric estimates of COVID-19 infections and deaths increased dramatically after March 2020. In March itself, however, when negative emotions were highest, our participants’ predictions of deaths and infections were quite low. Participants underestimated both deaths and infections, but their estimates increased as infections increased fairly well. Whereas the median estimate of infections was 13,500,000 by the end of the year, the actual number of infected in the U.S. in 2020 was over 20 million and the number of deaths exceeded 350,000 by December 31st. But estimates of cases and deaths didn’t correspond to people’s perceptions of their own likelihood of catching COVID-19 However, even as cases and deaths rose in April and later, participants’ estimates of their own likelihood of getting coronavirus remained steady. Logically, as cases rose, people should have thought that their likelihood of getting it was higher. These are the percentage who thought they were “somewhat likely” to “completely certain” to get coronavirus. Instead of relating to the numbers of cases and infections, the perceived likelihood of getting coronavirus tracked emotional reactions instead (see below). When people judged their own likelihood of COVID-19, they may have used their feelings to assess their risk more than they used the statistical rates of cases around them. These findings are consistent with the idea that knowledge of the numbers does not necessarily lead people to understand what those numbers mean for them. Indeed, as we’ve discussed in the New York Times, people who spent more time looking at statistics were more fearful and seemed to focus particularly on negative news, such as death rates vs. survival rates.

Doctors & clinicians, scientists, and the CDC were consistently trusted by most of our sample from March to December. In contrast, President Trump, the news media, and the American public were distrusted by the majority of our sample. Trust in President-elect Biden was lower than the CDC, doctors & clinicians, and scientists. This finding was due to trust in professionals being more bipartisan. Thus, these results provide a measure of hope—even in our increasingly polarized society, Americans still trust the experts when it comes to health recommendations . Our piece in The Conversation discusses in more detail why this trust is so important. However, even doctors are not immune from spreading disinformation as discussed in our piece on the Why Social Science blog and sometimes may need help in providing accurate numeric information to patients and others; see our “4 tips to help you figure out tricky stats” in The Conversation.