Authors
Weishun Zhong, Tankut Can, Antonis Georgiou, Ilya Shnayderman, Mikhail Katkov, Misha Tsodyks
Published in
Physical review letters. Volume 134. Issue 23. Pages 237402. Jun 13, 2025.
Abstract
Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across different narratives. We introduce a statistical ensemble of random trees to represent narratives as hierarchies of key points, where each node is a compressed representation of its descendant leaves, which are the original narrative segments. Recall from this hierarchical representation is constrained by working memory capacity. Our analytical solution aligns with observations from large-scale narrative recall experiments. Specifically, our model explains that (1) average recall length increases sublinearly with narrative length and (2) individuals summarize increasingly longer narrative segments in each recall sentence. Additionally, the theory predicts that for sufficiently long narratives, a universal, scale-invariant limit emerges, where the fraction of a narrative summarized by a single recall sentence follows a distribution independent of narrative length.
PMID:
40577734
Bibliographic data and abstract were imported from PubMed on 28 Jun 2025.
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