语言的层级结构能最优适应人类有限的工作记忆容量。
Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits
- 通过构建概率模型，发现层级处理使语言处理更契合工作记忆容量
- 相比线性处理，层级结构在长句中显著降低记忆负荷
- 结果与儿童工作记忆发展规律吻合，解释语言为何普遍具层级性
语言是人类独有的特征，通过将词序组织成层级结构高效传递信息。本研究揭示：语言的层级化设计正是为了解决人类有限工作记忆容量（WMC）的挑战。我们建立了一个似然函数，量化语言处理机制中单位数量平均值与人类工作记忆容量的匹配程度。最大似然估计（MLE）结果表明，该函数的最优解即为单位数量的均值。通过对符号序列的计算模拟及对自然语言句子的验证分析发现，相较于线性处理，层级处理在长序列下能更有效地将θ_MLE值控制在人类工作记忆极限内，并随句子长度增加仍保持良好表现。此外，该模式与儿童工作记忆发展呈现收敛趋势。结果表明，构建层级结构可在内存约束下最大化语言处理效率，真正解释了人类语言的普遍层级特性。
原文摘要 · Abstract (English)
Language is a uniquely human trait, conveying information efficiently by organizing word sequences in sentences into hierarchical structures. A central question persists: Why is human language hierarchical? In this study, we show that hierarchization optimally solves the challenge of our limited working memory capacity. We established a likelihood function that quantifies how well the average number of units according to the language processing mechanisms aligns with human working memory capacity (WMC) in a direct fashion. The maximum likelihood estimate (MLE) of this function, tehta_MLE, turns out to be the mean of units. Through computational simulations of symbol sequences and validation analyses of natural language sentences, we uncover that compared to linear processing, hierarchical processing far surpasses it in constraining the tehta_MLE values under the human WMC limit, along with the increase of sequence/sentence length successfully. It also shows a converging pattern related to children's WMC development. These results suggest that constructing hierarchical structures optimizes the processing efficiency of sequential language input while staying within memory constraints, genuinely explaining the universal hierarchical nature of human language.
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