Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits

• URL: http://arxiv.org/abs/2601.02740v1
• Date: Tue, 06 Jan 2026 06:05:47 GMT
• Title: Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits
• Authors: Luyao Chen, Weibo Gao, Junjie Wu, Jinshan Wu, Angela D. Friederici,
• Abstract summary: We show that hierarchization optimally solves the challenge of our limited working memory capacity.<n>Results suggest that constructing hierarchical structures optimize the processing efficiency of sequential language input.
• Score: 9.592127708776667
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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