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Related Concept Videos

Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Observational Learning01:12

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Related Experiment Video

Updated: Jul 17, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Game dynamics with learning and evolution of universal grammar.

W Garrett Mitchener1

  • 1Department of Mathematics, College of Charleston, Robert Scott Small Building, Room 339, Charleston, SC 29424, USA. MitchenerG@cofc.edu

Bulletin of Mathematical Biology
|February 1, 2007
PubMed
Summary

Language evolution is modeled using game dynamics. Universal grammar (UG) variants are evolutionarily stable if new grammars are incompatible, suggesting historical factors, not just fitness, shape language genetics.

Related Experiment Videos

Last Updated: Jul 17, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Area of Science:

  • Evolutionary biology
  • Linguistics
  • Game theory

Background:

  • The human language faculty is rooted in universal grammar (UG).
  • Understanding the evolutionary dynamics of UG is crucial for explaining language diversity.

Purpose of the Study:

  • To model language evolution using population game dynamics and learning.
  • To determine the conditions under which different variants of universal grammar (UG) are evolutionarily stable.

Main Methods:

  • Utilized dynamical systems techniques to analyze a communication game model.
  • Investigated scenarios with two and an arbitrary number of disjoint UGs.
  • Examined the impact of payoff matrix constraints and learning processes.

Main Results:

  • Proved that two UG variants are evolutionarily stable if payoff constraints are met, independent of learning.
  • Extended this to an arbitrary number of disjoint UGs.
  • Demonstrated that compatible mutations' success depends on population history and learning.

Conclusions:

  • The genetic history of language is constrained by grammar compatibility requirements.
  • Mutations in the language faculty may be fixed or lost due to historical contingency rather than solely relative fitness.