Random Tree Model of Meaningful Memory

TL;DR

This paper introduces a random tree model to represent narratives hierarchically, explaining key features of memory recall such as sublinear recall length growth and universal summarization patterns across different narratives.

Contribution

The paper presents a novel statistical ensemble of random trees to model narrative recall, providing analytical insights into memory constraints and universal scaling behaviors.

Findings

Recall length grows sublinearly with narrative length

Individuals summarize longer segments in each recall

Universal, scale-invariant distribution emerges for long narratives

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 is modeled as constrained by working memory capacity from this hierarchical structure. 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.