ArticleProceedings of the National Academy of Sciences of the United States of America2024
Detecting recollection: Human evaluators can successfully assess the veracity of others' memories.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Shared memories of event details in the human brain are altered by misinformation and test expectations.PLoS biology · 2026Article
- The detection of episodic memory in others biases social choice.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Memory justifications provide valid indicators of retrieval accuracy across time.Communications psychology · 2025Article
- AI assistance improves people's ability to distinguish correct from incorrect eyewitness lineup identifications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Perceived memory credibility: The role of details.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Humans have the highly adaptive ability to learn from others' memories. However, because memories are prone to errors, in order for others' memories to be a valuable source of information, we need to assess their veracity. Previous studies have shown that linguistic information conveyed in self-reported justifications can be used to train a machine-learner to distinguish true from false memories. But can humans also perform this task, and if so, do they do so in the same way the machine-learner does? Participants were presented with justifications corresponding to Hits and False Alarms and were asked to directly assess whether the witness's recognition was correct or incorrect. In addition, participants assessed justifications' recollective qualities: their vividness, specificity, and the degree of confidence they conveyed. Results show that human evaluators can discriminate Hits from False Alarms above chance levels, based on the justifications provided per item. Their performance was on par with the machine learner. Furthermore, through assessment of the perceived recollective qualities of justifications, participants were able to glean more information from the justifications than they used in their own direct decisions and than the machine learner did.
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Registered trials
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