How Our Deepest Beliefs About Knowledge Shaped the Pandemic Response
Imagine logging onto social media during the COVID-19 pandemic only to find your feed split between starkly different realities. In one, respected epidemiologists discussed vaccine efficacy; in another, videos questioned whether the virus was real at all.
This information ecosystem created what the World Health Organization termed an "infodemic"—an overabundance of information, both accurate and false, that spread alongside the virus itself 2 . What determined which version of reality people embraced? Groundbreaking research reveals that our epistemic beliefs—our fundamental assumptions about what constitutes knowledge and how we should obtain it—played a crucial role in shaping how we viewed science, news, and policymaking during the global crisis 1 .
An overwhelming amount of COVID-19 information made it difficult for people to distinguish facts from misinformation, creating parallel information realities.
Deeply held assumptions about knowledge shaped how individuals processed scientific information and responded to public health guidance.
At the heart of the pandemic information divide lies the concept of epistemic beliefs—our deeply held assumptions about the nature, sources, and limits of knowledge. These beliefs form a hidden framework that guides how we process new information, particularly in uncertain situations.
Closely related to epistemic beliefs are our informational needs—the ways we prefer to receive and engage with information. The pandemic revealed two primary orientations:
To understand how epistemic beliefs shaped responses to pandemic information, researchers conducted a sophisticated online survey of 1,513 German residents, carefully designed to be representative of the broader population in age, gender, education, and place of residence 1 .
Participants completed validated psychological scales measuring their views on whether scientific knowledge is static or dynamic.
Researchers gauged participants' desire for cognitive closure versus their comfort with uncertainty.
Participants shared their views on the appropriate roles for scientists in policymaking.
Advanced techniques including latent profile analysis identified subgroups with similar belief patterns.
The analysis revealed four distinct profiles based on how people believed COVID-19 information 5 .
| Profile | Percentage | Belief in Science | Key Characteristics |
|---|---|---|---|
| Science Accepters | 70% | High | Believed scientific consensus, rejected misinformation |
| Multiple Belief Holders | 13% | High | Accepted scientific consensus but also believed some misinformation |
| Conspiracy Inclined | 11% | High | Accepted science but strongly believed alternative narratives |
| Skeptical | 6% | Neutral | Showed inconsistent belief patterns |
Perhaps the most striking finding was that trust in science emerged as a powerful predictor of belief profile membership, with lower trust being substantively associated with belonging to Profiles 2 through 4 5 .
Understanding complex human behaviors during the pandemic required sophisticated research tools. Just as biomedical researchers rely on laboratory reagents, behavioral scientists employ standardized psychological instruments and methodologies.
| Research "Reagent" | Primary Function | Application in Pandemic Studies |
|---|---|---|
| Validated Epistemic Belief Scales | Measure fundamental assumptions about knowledge | Identified static vs. dynamic knowledge beliefs |
| Information Need Inventories | Assess preference for closure vs. construction | Predicted tolerance for evolving guidelines |
| Latent Profile Analysis | Identify subgroups with similar belief patterns | Revealed four COVID-19 belief profiles |
| Trust in Science Scales | Quantify confidence in scientific institutions | Strongly predicted resistance to misinformation |
| Cognitive Reflection Tests | Measure analytical vs. intuitive thinking | Correlated with better truth discernment |
These research tools enabled scientists to move beyond superficial demographics and uncover the psychological architecture that shaped pandemic responses across global populations 5 8 .
Research confirmed that susceptibility to COVID-19 misinformation was strongly influenced by both cognitive and social factors 8 .
The interaction between scientific advice and policymaking varied significantly across national contexts during the pandemic 6 .
Evidence-based science communication is unlikely to produce the negative consequences that some policymakers fear 9 .
A massive synthesis of evidence for policy from behavioural science during COVID-19 examined 747 pandemic-related research articles to determine which communication strategies were most effective 9 .
The COVID-19 pandemic revealed profound divisions in how people perceive, evaluate, and use scientific information. Rather than being driven solely by political polarization or educational disparities, these divisions reflect fundamentally different epistemic beliefs about the nature of knowledge itself.
As we face future global challenges—from climate change to emerging pathogens—understanding these psychological dynamics becomes increasingly crucial. The research clearly indicates that building public trust in science may be more effective than simply countering misinformation with facts 5 8 .
Effective science communication cannot follow a one-size-fits-all approach. Recognizing the diversity of informational needs and epistemic beliefs—and developing strategies that address this full spectrum—may prove essential for building more resilient societies.