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Integrating explicit reliability for optimal choices: effect of trustworthiness on decisions and metadecisions

Keiji Ota,Anthony Ciston,P. Haggard,Thibault Gajdos Preuss,Lucie Charles

Published 2026 in bioRxiv

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 and the extent to which individuals have metacognitive insight into this process. We developed a novel paradigm where participants viewed opinions from sources of varying reliability to 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 and how leaky their decision process was. Overall, we found that participants acted as if sources were more informative than they actually were, inflating the reliability they were communicated. Interestingly, we show that even though sources were explicitly labelled as unreliable, these sources biased choices, as if these were treated as moderately reliable. Additionally, the presence of sources known to be lying, reliably voting for the incorrect answer, impaired performance by increasing decision leakiness. Despite these biases, participants showed some metacognitive awareness of what influenced their choices: they were generally accurate in reporting the degree to which a source influenced them and were aware of the impact unreliable sources had on their decisions. These results suggest that people make suboptimal use of explicit source reliability, but have some awareness of their suboptimal choices. 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 fell short of optimal. First, sources that were clearly identified as unreliable still influenced the opinions 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.

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