  One of the most impressive achievements of the AI revolution is the
development of large language models that can generate meaningful text and
respond to instructions in plain English with no additional training necessary.
Here we show that language models can be used as a scientific instrument for
studying human memory for meaningful material. We developed a pipeline for
designing large scale memory experiments and analyzing the obtained results. We
performed online memory experiments with a large number of participants and
collected recognition and recall data for narratives of different lengths. We
found that both recall and recognition performance scale linearly with
narrative length. Furthermore, in order to investigate the role of narrative
comprehension in memory, we repeated these experiments using scrambled versions
of the presented stories. We found that even though recall performance declined
significantly, recognition remained largely unaffected. Interestingly, recalls
in this condition seem to follow the original narrative order rather than the
scrambled presentation, pointing to a contextual reconstruction of the story in
memory.
