Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits

TL;DR

This paper demonstrates that hierarchical structuring in language optimally addresses human working memory limits, explaining why language is inherently hierarchical through computational and empirical evidence.

Contribution

It introduces a likelihood-based framework showing hierarchical language processing maximizes efficiency within working memory constraints, a novel explanation for language structure.

Findings

Hierarchical processing better constrains memory limits than linear processing.

Sequence length and development stages influence hierarchical processing efficiency.

Natural language sentences exhibit patterns consistent with the model's predictions.

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.