用随机树模型解释叙事记忆的规律，发现回忆随故事变长而增速放缓。
Random Tree Model of Meaningful Memory
- 将叙事建模为分层的随机树结构，节点压缩子节点信息
- 回忆长度随故事长度增加但增速递减，每句回忆覆盖更长段落
- 长故事下出现普适的缩写比例分布，与故事长度无关
传统对有意义叙事记忆的研究聚焦于具体故事及其语义结构，但未揭示跨不同叙事的共性定量特征。本文引入随机树的统计系综，将叙事表示为关键点的层次结构，每个节点是其子叶（原始叙事片段）的压缩表示。回忆过程受工作记忆容量约束，由该层次结构决定。解析解与大规模叙事回忆实验观测一致：（1）平均回忆长度随叙事长度增长呈亚线性关系；（2）个体在每句回忆中总结的叙事段落越来越长。此外，理论预测当叙事足够长时，会涌现出一个普适的、尺度不变的极限，单句回忆所概括的叙事比例分布不再依赖叙事长度。
原文摘要 · Abstract (English)
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.
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