标题： 有意义记忆的随机树模型 显示英文标题

标题： Random Tree Model of Meaningful Memory

摘要： 传统上对有意义叙述的记忆研究集中在特定故事及其语义结构上，但并未涉及不同叙述之间回忆的共同定量特征。 我们引入了一个随机树的统计系综，将叙述表示为关键点的层次结构，其中每个节点是其后代叶节点的压缩表示，这些叶节点是原始叙述片段。 回忆被建模为受由此层次结构的工作记忆容量约束。 我们的解析解与大规模叙述回忆实验的观察结果一致。 具体而言，我们的模型解释了以下两点：(1) 平均回忆长度随着叙述长度的增加呈次线性增长，(2) 个体在每次回忆句子中总结越来越长的叙述片段。 此外，该理论预测，对于足够长的叙述，会出现一个普遍的、尺度不变的极限，其中单个回忆句子所总结的叙述部分的分布与叙述长度无关。 显示英文摘要

摘要： 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.