Readers rapidly check new information against prior knowledge during validation, but research is inconsistent as to whether source credibility affects validation. We argue that readers are likely to accept highly plausible assertions regardless of source, but that high source credibility may boost acceptance of claims that are less plausible based on general world knowledge. In Experiment 1, participants read narratives with assertions for which the plausibility varied depending on the source. For high credibility sources, we found that readers were faster to read information confirming these assertions relative to contradictory information. We found the opposite patterns for low credibility characters. In Experiment 2, readers read claims from the same high or low credibility sources, but the claims were always plausible based on general world knowledge. Readers consistently took longer to read contradictory information, regardless of source. In Experiment 3, participants read modified versions of "The Tell-Tale Heart," which was narrated entirely by an unreliable source. We manipulated the plausibility of a target event, as well as whether high credibility characters within the story provided confirmatory or contradictory information about the narrator's description of the target event. Though readers rated the narrator as being insane, they were more likely to believe the narrator's assertions about the target event when it was plausible and corroborated by other characters. We argue that sourcing research would benefit from focusing on the relationship between source credibility, message credibility, and multiple sources within a text.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3758/s13421-016-0656-1 | DOI Listing |
Am J Public Health
January 2025
Alexia Couture, A. Danielle Iuliano, Ryan Threlkel, Matthew Gilmer, Alissa O'Halloran, Dawud Ujamaa, Matthew Biggerstaff, and Carrie Reed are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA. Howard H. Chang is with the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFObjective: To explore patients' perceptions and attitudes towards patient guidelines (PGs) and to identify specific factors related to PG content, design, presentation, and management that may influence patients' use or adoption of PGs.
Methods: An exploratory sequential mixed-methods design was employed. Initial semi-structured interviews were conducted with a diverse group of individuals, including people with diabetes or oncology, and clinicians.
Implement Res Pract
January 2025
Department of Psychology, Temple University, Philadelphia, PA, USA.
Background: Dissemination initiatives have the potential to increase consumer knowledge of and engagement with evidence-based treatments (e.g., cognitive behavioral therapy [CBT]).
View Article and Find Full Text PDFBMC Public Health
January 2025
Ministère de la Santé et de l'Action Sociale (MHSA), Dakar, Senegal.
Introduction: In Senegal, the Routine Health Information System (RHIS) captures the majority of data from the Ministry of Health and Social Action (MHSA) public structures and very little health data from the private sector and other ministerial departments. Quality data strengthens the validity and reliability of research results. Common areas of data quality include accuracy, completeness, consistency, credibility, and timeliness.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!