Detecting recollection: Human evaluators can successfully assess the veracity of others’ memories

Avi Gamoran, Lilach Lieberman, Michael Gilead, Ian G. Dobbins, Talya Sadeh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere2310979121
JournalProceedings of the National Academy of Sciences of the United States of America
Volume121
Issue number22
DOIs
StatePublished - 28 May 2024

Keywords

  • language
  • machine learning
  • memory justifications
  • recognition memory

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