How do we evaluate others’ memories? A behavioral and natural language processing study

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 do humans process this information in the same way the model does? Participants were presented with justifications corresponding to Hits and False-Alarms and were asked to assess whether the witness’s recognition was correct or incorrect. Results show that human evaluators can discriminate Hits from False Alarms above chance levels, based on the justifications provided per item. Classification using indirect measures (Confidence, Opinion-ratings) outperformed evaluators’ explicit classification. Predictions from the indirect classification model shared only a portion of the variance with the predictions of the machine learning language-based model. Hence, features generated from humans’ assessments augmented the language-based models, with the model combining both humans’ and the machine-learner’s classifications outperforming each model individually.