{ "abstract": "A key challenge in today’s fast-paced digital world is to integrate information from various sources, which differ in their reliability. Yet, little is known about how explicit probabilistic information about the likelihood that a source provides correct information is used in decision-making. Here, we investigated how such explicit reliability markers are integrated into decisions and the extent to which individuals have metacognitive insight into this process. We developed a novel paradigm where participants viewed predictions from sources of varying explicit reliability to help them make a choice between two options. After each decision, they rated how much they felt a given source influenced their choice. Using computational modelling, we estimated the effective reliability that participants assigned to each source. Overall, we found that participants did not take source reliability at face value, inappropriately using the stated probabilities of source correctness to make a choice. Interestingly, even sources that were explicitly labelled as unreliable biased choices, as if these were treated as moderately reliable. Additionally, the presence of sources flagged as lying ones and known to be reliably predicting the incorrect answer impaired performance by increasing leakiness in evidence accumulation. Despite these biases, participants showed some metacognitive awareness of what influenced their choices: they were aware of the impact unreliable sources had on their decisions and could evaluate how much a given source had increased the likelihood of their response. These results suggest that people distort explicit source reliability but have some awareness of this process. Author summary: In everyday life, we constantly draw on information from multiple sources that differ in how much they can be trusted. When scrolling through social media or online platforms, for instance, we know some sources are reliable, others dubious, and some clearly misleading. In this study, we created a simple task mirroring this everyday challenge to examine how people form beliefs using information from sources of known, varying reliability. Using a computational model, we uncovered two systematic ways in which decisions are biased. First, sources that were clearly identified as unreliable still influenced the beliefs and final choice of individuals. Second, people’s choices were often swayed by the last piece of information they saw, especially in a context where misleading sources were present. Surprisingly, participants were aware of some of these biases and could report which sources had shaped their choices, suggesting that awareness of bias is not necessarily sufficient to overcome it.", "abstract_shown": true, "affiliations": [ "Institute of Cognitive Neuroscience, University College London, London, United Kingdom", "Department of Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom", "Institute of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan", "Advanced Research Initiative for Human High Performance, University of Tsukuba, Tsukuba, Japan", "Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany" ], "article_type": "Research Article", "arxiv": null, "author_total": 5, "authors": [ { "affs": [ 0 ], "name": "Keiji Ota" }, { "affs": [ 1 ], "name": "Anthony Ciston" }, { "affs": [ 2 ], "name": "Patrick Haggard" }, { "affs": [ 3 ], "name": "Thibault Gajdos Preuss" }, { "affs": [ 4 ], "name": "Lucie Charles" } ], "bibtex": "https://research.draconitic.com/paper/94q7dazeqvz26.bib", "cited_by": 1, "doi": "10.1371/journal.pcbi.1014818", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014818", "id": "94q7dazeqvz26", "issns": [ "1553-7358" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014818", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1800|Decision-Making and Behavioral Economics" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Decision making" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Metacognition" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Decision making" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Decision making" ], [ "plos", "/Physical sciences/Mathematics/Probability theory/Probability distribution/Normal distribution" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Monte Carlo method" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Regression analysis/Linear regression analysis" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical noise/Gaussian noise" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Monte Carlo method" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Regression analysis/Linear regression analysis" ], [ "plos", "/Research and analysis methods/Simulation and modeling" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Decision making" ], [ "plos", "/Social sciences/Sociology/Communications/Social communication" ] ], "openalex": "W7221065375", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014818&type=printable", "pmcid": null, "pmid": null, "published": "2026-10-09", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/94q7dazeqvz26.ris", "sources": [ { "first_seen": "2026-10-30T12:17:30Z", "native_id": "10.1371/journal.pcbi.1014818", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014818" } ], "subjects": [ { "field": "biology", "origin": "plos:neuroscience", "subfield": "neuroscience" }, { "field": "mathematics", "origin": "plos:statistical methods", "subfield": "statistics" } ], "title": "How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition", "title_text": "How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:30Z", "url": "https://research.draconitic.com/paper/94q7dazeqvz26", "venue": "PLOS Computational Biology", "versions": [] }