Prior Beliefs not Cognitive Reflection Predict People’s Accuracy when Judging Health (Mis)Information

Health misinformation affects individual and public health decisions and erodes trust in medical institutions. Online, people are often confronted with a large variety of both evidence-based and misleading content, making credibility assessments challenging. Judgment errors are not simply the result of a lack of access to accurate information but depend on a variety of cognitive, social and affective factors. In this study, we asked a quota-representative sample of the Austrian population (N = 343) to assess the veracity and credibility of 50 health claims drawn from real-world online information. Using a signal-detection approach, we examined which individual differences predicted accuracy, perceived credibility and response bias. Results indicate that stronger belief in alternative medicine and in conspiracy theories were associated with lower accuracy, whereas cognitive reflection, motivation for cognition and intellectual humility were not. Stronger belief in alternative medicine, more trust in science, and older age were also associated with being more likely to believe any information regardless of veracity. Overall, our results point towards prior beliefs, not cognitive variables, playing a larger role in what information people choose to believe. Implications for interventions are discussed.

Authors

Publication Details

Published
2026-10-30
DOI
https://doi.org/10.31234/osf.io/5tfwc_v1
Primary Topic
Misinformation and Its Impacts
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Prior Beliefs not Cognitive Reflection Predict People’s Accuracy when Judging Health (Mis)Information

Matthias Berking, Hilmar Brohmer, Michael A. Bedek, Dietrich Albert et al.
2 links
Misinformation and Its Impacts
preprint

Prior Beliefs not Cognitive Reflection Predict People’s Accuracy when Judging Health (Mis)Information

Matthias Berking, Hilmar Brohmer, Michael A. Bedek, Dietrich Albert, Marie-Therese Hoesch, Sarah Annabelle von Götz
preprint en

Abstract

Health misinformation affects individual and public health decisions and erodes trust in medical institutions. Online, people are often confronted with a large variety of both evidence-based and misleading content, making credibility assessments challenging. Judgment errors are not simply the result of a lack of access to accurate information but depend on a variety of cognitive, social and affective factors. In this study, we asked a quota-representative sample of the Austrian population (N = 343) to assess the veracity and credibility of 50 health claims drawn from real-world online information. Using a signal-detection approach, we examined which individual differences predicted accuracy, perceived credibility and response bias. Results indicate that stronger belief in alternative medicine and in conspiracy theories were associated with lower accuracy, whereas cognitive reflection, motivation for cognition and intellectual humility were not. Stronger belief in alternative medicine, more trust in science, and older age were also associated with being more likely to believe any information regardless of veracity. Overall, our results point towards prior beliefs, not cognitive variables, playing a larger role in what information people choose to believe. Implications for interventions are discussed.

Good health and well-being
Misinformation and Its Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Prior Beliefs not Cognitive Reflection Predict People’s Accuracy when Judging Health (Mis)Information — Matthias Berking, Hilmar Brohmer, et al. · (2026) | TGRS Research Map | TGRS