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Human languages exhibit systematic structures, like words and phrases, which emerge in complex codes. Minimizing predictive information, a measure of complexity, reveals how these natural language features develop.

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Area of Science:

  • Computational Linguistics
  • Information Theory
  • Cognitive Science

Background:

  • Human language possesses a systematic structure, with words forming phrases.
  • The statistical properties underlying this structure are not fully understood.

Purpose of the Study:

  • To investigate the emergence of natural-language-like systematicity in codes.
  • To explore the role of predictive information (excess entropy) in shaping linguistic structures.
  • To link statistical properties of language to cognitive constraints.

Main Methods:

  • Simulations of codes constrained by predictive information.
  • Analysis of cross-linguistic text corpora.
  • Comparison of human language complexity with baseline models.

Main Results:

  • Codes minimizing predictive information develop systematic, locally expressed features resembling words and phrases.
  • Human languages exhibit low predictive information across phonology, morphology, syntax, and lexical semantics.
  • A link is established between statistical/algebraic language structure and communication constraints.

Conclusions:

  • Natural-language-like systematicity can arise from minimizing predictive information.
  • Human languages are structured to be statistically efficient, likely shaped by cognitive and communicative pressures.
  • This work bridges information theory and linguistics, offering insights into language evolution.