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Summary

Human languages optimize sentence structure to reduce cognitive load. A new score reveals that 50% of languages achieve over 70% optimization, with longer sentences showing greater efficiency.

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Network Science

Background:

  • Human languages, like biological systems, are thought to evolve under cost-saving pressures.
  • Quantifying language optimality, particularly word order, has been limited, with few studies focusing on English.

Purpose of the Study:

  • To develop and apply a novel score to quantify the degree of sentence word order optimization across diverse languages.
  • To investigate the extent to which cognitive pressures influence syntactic structure in human languages.

Main Methods:

  • Recasting sentence word order optimality as an optimization problem on a spatial network model.
  • Defining vertices as words, arcs as syntactic dependencies, and space by linear word order.
  • Introducing a score to measure cognitive pressure for reducing distances between linked words.

Main Results:

  • Analysis of 93 languages across 19 families shows 50% are optimized at 70% or higher.
  • The score confirms that longer sentences are generally more optimized.
  • Short sentences exhibit longer word distances than statistically expected, while few languages show minimal distance reduction.

Conclusions:

  • The developed score provides a hierarchical ranking of languages by optimization degree.
  • Findings have implications for dependency linguistics, typology, historical linguistics, clinical linguistics, and cognitive science.
  • The score's design principles offer insights applicable to network science research.