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Random Tree Model of Meaningful Memory

Weishun Zhong1, Tankut Can1,2, Antonis Georgiou1,3, Ilya Shnayderman3, Mikhail Katkov1,3, and Misha Tsodyks1,3,*

  • *Contact author: mtsodyks@gmail.com
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Phys. Rev. Lett. 134, 237402 – Published 13 June, 2025

DOI: https://doi.org/10.1103/g1cz-wk1l

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.

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How We Remember Stories

Published 13 June, 2025

The memory of a story appears to have a tree-like structure, with abstract summaries branching out into more specific details.

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Outline

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Phys. Rev. Lett. 134, 237402– Published 13 June, 2025
  • Received 11 December 2024
  • Accepted 30 April 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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