How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition

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.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-10-09
DOI
https://doi.org/10.1371/journal.pcbi.1014818
Primary Topic
Decision-Making and Behavioral Economics
Type
article
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article

How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition

Thibault Gajdos, Lucie Charles, Keiji Ota, Anthony Ciston et al.
PLoS Computational Biology
Decision-Making and Behavioral Economics
article

How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition

Thibault Gajdos, Lucie Charles, Keiji Ota, Anthony Ciston, Patrick Haggard
article en

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.

PLoS Computational BiologyVol. 22(10)
Centre National de la Recherche Scientifique (FR), University of Tsukuba (JP), Queen Mary University of London (GB), Aix-Marseille Université (FR), Max Planck Institute for Human Cognitive and Brain Sciences (DE), University College London (GB)
Openalex Percentile: Top 6%
Decision-Making and Behavioral Economics
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