CogSci 2025

•

August 01, 2025

•

San Francisco, United States

keywords:

quantitative behavior

big data

memory

natural language processing

Memory has traditionally been studied in well-controlled laboratory environments, which, while effective, do not fully capture the range of dynamics and behaviors shown in real-world contexts. To address this gap, we propose using summarization as a novel task to study memory recall in naturalistic settings. We argue that a key component of summarization is the ability to represent and retain information from the original material. Inspired by approaches in the free recall literature to analyze temporal dynamics of memory recall, such as how recall begins and transitions to subsequent items, we analyzed the temporal dynamics of summary patterns. Using three publicly available summarization datasets and a naturalistic narrative recall dataset, we found alignments between the summarization patterns and established free recall patterns, including primacy, recency, temporal contiguity, and the effect of list length. These results support that summarization involves processes of memory recall and open up opportunities to use summarization as a naturalistic task to study memory recall in the future.

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