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Modelling language evolution: Examples and predictions.

Tao Gong1, Lan Shuai2, Menghan Zhang3

  • 1Department of Linguistics, University of Hong Kong, Pokfulam Road, Hong Kong.

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|November 30, 2013
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Summary
This summary is machine-generated.

Computer models reveal how language evolves, showing links between general abilities and language skills. Future research should integrate experimental data and interdisciplinary collaboration for deeper insights into language evolution.

Keywords:
Complex adaptive systemComputer modellingEquation-based modelEvolutionary linguisticsRule-based model

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Area of Science:

  • Computational Linguistics
  • Evolutionary Biology
  • Cognitive Science

Background:

  • Language evolution is a complex process involving linguistic structures, individual learning, and socio-cultural factors.
  • Computer modeling offers a powerful lens to investigate these intricate dynamics.

Purpose of the Study:

  • To survey recent computer modeling research on language evolution.
  • To discuss key predictions derived from rule-based and equation-based models.
  • To identify future research directions in computational language evolution.

Main Methods:

  • Review of rule-based models simulating lexicon-syntax coevolution.
  • Analysis of equation-based models quantifying language competition dynamics.
  • Discussion of model predictions regarding cognitive abilities, cultural transmission, and cross-domain commonalities.

Main Results:

  • Models predict correlations between domain-general abilities (e.g., sequential learning) and language-specific mechanisms (e.g., word order processing).
  • Coevolution of language with competences like joint attention is suggested.
  • Cultural transmission and social structures impact linguistic understandability.
  • Linguistic phenomena share commonalities with biological and physical systems.

Conclusions:

  • Computer models significantly advance understanding of language structure evolution, learning mechanisms, and socio-cultural influences.
  • Future directions include experimental model evaluation, stronger empirical foundations, and multidisciplinary collaboration.