Studying Human Memory for Random and Meaningful Material: A Comparative Study

Event Details

Type

Center for Studies in Physics and Biology Seminars

Speaker(s)

Michail Tsodyks, Ph.D., C.V. Starr Professor, Institute for Advanced Studies

Speaker bio(s)

We consider the recognition and recall experiments on random lists of words vs meaningful narratives. A mathematical model based on a specific recall algorithm of random lists established the universal relation between the number of words that is retained in memory and the number of words that can on average be recalled, characterized by a square root scaling. This relation is expressed by an analytical expression with no free parameters and was confirmed experimentally to a surprising precision in online experiments. In order to extend this research to meaningful narratives, we took advantage of recently developed large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary. 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.

Open to

Tri-Institutional