Pairing facts with imagined consequences improves pandemic-related risk perception.

Journal Article (Journal Article)

The COVID-19 pandemic reached staggering new peaks during a global resurgence more than a year after the crisis began. Although public health guidelines initially helped to slow the spread of disease, widespread pandemic fatigue and prolonged harm to financial stability and mental well-being contributed to this resurgence. In the late stage of the pandemic, it became clear that new interventions were needed to support long-term behavior change. Here, we examined subjective perceived risk about COVID-19 and the relationship between perceived risk and engagement in risky behaviors. In study 1 (n = 303), we found that subjective perceived risk was likely inaccurate but predicted compliance with public health guidelines. In study 2 (n = 735), we developed a multifaceted intervention designed to realign perceived risk with actual risk. Participants completed an episodic simulation task; we expected that imagining a COVID-related scenario would increase the salience of risk information and enhance behavior change. Immediately following the episodic simulation, participants completed a risk estimation task with individualized feedback about local viral prevalence. We found that information prediction error, a measure of surprise, drove beneficial change in perceived risk and willingness to engage in risky activities. Imagining a COVID-related scenario beforehand enhanced the effect of prediction error on learning. Importantly, our intervention produced lasting effects that persisted after a 1- to 3-wk delay. Overall, we describe a fast and feasible online intervention that effectively changed beliefs and intentions about risky behaviors.

Full Text

Duke Authors

Cited Authors

  • Sinclair, AH; Hakimi, S; Stanley, ML; Adcock, RA; Samanez-Larkin, GR

Published Date

  • August 10, 2021

Published In

Volume / Issue

  • 118 / 32

PubMed ID

  • 34341120

Pubmed Central ID

  • PMC8364212

Electronic International Standard Serial Number (EISSN)

  • 1091-6490

Digital Object Identifier (DOI)

  • 10.1073/pnas.2100970118


  • eng

Conference Location

  • United States