Evidence map›Paper›PMID 41261846›Full record

ArticleCognitive science2025

The Bias-and-Expertise Model: A Bayesian Network Model of Political Source Characteristics.

David J Young, Lee H de-Wit

Abstract read
In one paragraph

Article in Cognitive science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

David J YoungDepartment of Psychology, University of Cambridge.
Lee H de-WitDepartment of Psychology, University of Cambridge.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perceptions of source credibility may play a role in major societal challenges like political polarization and the spread of misinformation as citizens disagree over which sources of political information are credible and sometimes trust untrustworthy sources. Cognitive scientists have developed Bayesian Network models of how people integrate perceptions of source credibility when learning from information provided by sources, but these models do not involve the crucial source characteristic in politics: bias. Biased sources make claims that align with a particular political agenda, whether or not they are true. We present a novel Bayesian Network model which integrates perceptions of a source's bias as well as their expertise. We demonstrate the model's validity for predicting how people will update beliefs and perceptions of bias and expertise in response to testimony across two studies, the second being a preregistered conceptual replication and extension of the first.

Indexed as

Models, PsychologicalPoliticsAdultBayes TheoremBiasFemaleHumansMaleTrustBayesianBiasCharacteristicsExpertiseNetworkPolarizationPoliticalSource

Identifiers

PMID41261846
PMCPMC12631054

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.