Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits
🤖 AI Summary
Human languages universally exhibit hierarchical structure, likely shaped by constraints imposed by limited working memory capacity during language processing. This study establishes the first direct link between hierarchical organization and working memory optimization, proposing that hierarchical parsing constitutes an optimal strategy for maximizing processing efficiency under memory constraints. By formulating a likelihood function and integrating maximum likelihood estimation, computational simulations of symbolic sequences, and validation against natural language corpora, the authors quantify the average number of processing units required under linear versus hierarchical parsing strategies. Results demonstrate that hierarchical processing consistently maintains the number of processing units within the bounds of human working memory capacity even as sentence length increases, significantly outperforming linear processing. Moreover, the performance trajectory of hierarchical parsing closely aligns with empirical data on children’s working memory development.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
language hierarchization
working memory capacity
hierarchical structure
memory constraints
language processing
Innovation
Methods, ideas, or system contributions that make the work stand out.
language hierarchization
working memory capacity
maximum likelihood estimation
computational simulation
hierarchical processing
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