Search

Abstract Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.

Models of heuristics are often predicated on the desideratum that they should possess no free parameters. As a result, heuristic implementations are usually deterministic and do not allow for any choice errors, as the latter would require a parameter to regulate the magnitude of errors. We discuss the implications of this in light of research that highlights the evidence supporting stochastic choice and its dependence on preferential strength. We argue that, in principle, the existing models of deterministic heuristics should, and can, be quite easily modified to stochastic counterparts through the addition of an error mechanism. This requires a single free parameter in the error mechanism, whilst otherwise retaining the parameter-free cognitive processes in the deterministic component of existing heuristics. We present various types of error mechanisms applicable to heuristics and discuss their comparative virtues and drawbacks, paying particular attention to their impact on model comparisons between heuristics and parameter-rich models.

BackgroundAlthough transparency is crucial for building public trust, public health communication during the COVID-19 pandemic was often nontransparent.MethodsIn a cross-sectional online study with COVID-19 vaccine-hesitant German residents (N = 763), we explored the impact of COVID-19 public health communication on the attitudes of vaccine-hesitant individuals toward vaccines as well as their perceptions of incomprehensible and incomplete information. We also investigated whether specific formats of public health messaging were perceived as more trustworthy.ResultsOf the 763 participants, 90 (11.8%) said they had become more open-minded toward vaccines in general, 408 (53.5%) reported no change, and 265 (34.7%) said they had become more skeptical as a result of public health communication on COVID-19 vaccines. These subgroups differed in how incomprehensible they found public health communication and whether they thought information had been missing. Participants’ ranking of trustworthy public health messaging did not provide clear-cut results: the fully transparent message, which reported the benefit and harms in terms of absolute risk, and the nontransparent message, which reported only the benefit in terms of relative risk were both considered equally trustworthy (p = 0.848).DiscussionIncreased skepticism about vaccines during the COVID-19 pandemic may have partly been fueled by subpar public health communication. Given the importance of public trust for coping with future health crises, public health communicators should ensure that their messaging is clear and transparent.