The Computational Nature of Language Learning and Evolution

@inproceedings{Niyogi2006TheCN,
  title={The Computational Nature of Language Learning and Evolution},
  author={Partha Niyogi},
  year={2006}
}
The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomenon of language acquisition; the evolution of a language, however, is not so directly experienced. Language learning by children is robust and reliable, but it cannot be perfect or languages would never change -- and English, for example, would not have evolved from the language of the Anglo-Saxon… 
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References

SHOWING 1-5 OF 5 REFERENCES
Evolution of universal grammar.
TLDR
This work presents a mathematical framework for the evolutionary dynamics of grammar learning and calculates the condition under which natural selection favors the emergence of rule-based, generative grammars that underlie complex language.
Language Identification in the Limit
  • E. M. Gold
  • Linguistics, Computer Science
    Inf. Control.
  • 1967
Cultural transmission and evolution: a quantitative approach.
TLDR
A mathematical theory of the non-genetic transmission of cultural traits is developed that provides a framework for future investigations in quantitative social and anthropological science and concludes that cultural transmission is an essential factor in the study of cultural change.
He works on computational modeling of concept acquisition under the influence of language; he then uses insights gained from this to construct cognitive systems for AI and robotic applications
    Language identification
    • 1967