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An artificial life approach to language

D Parisi1

  • 1Institute of Psychology, National Research Council, Rome, Italy. domenico@kant.irmkant.rm.cnr.it

Brain and Language
|August 1, 1997
PubMed
Summary

Artificial Life Neural Networks (ALNNs) offer a new perspective on language research, moving beyond traditional models. This approach emphasizes language evolution, transmission, and its role in cognition within populations.

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Area of Science:

  • Computational Linguistics
  • Artificial Life
  • Cognitive Science

Background:

  • Traditional language research paradigms include symbolic and connectionist approaches.
  • These paradigms often focus on linguistic forms and laboratory tasks.
  • A gap exists in understanding language as an emergent, evolving, and populational phenomenon.

Purpose of the Study:

  • To demonstrate how an Artificial Life approach alters the language research agenda.
  • To highlight the distinctions between Artificial Life Neural Networks (ALNNs) and classical connectionist networks.
  • To shift focus towards the dynamic and ecological aspects of language.

Main Methods:

  • Utilizing Artificial Life Neural Networks (ALNNs) that interact with physical environments.
  • Modeling evolutionary, developmental, and cultural changes alongside learning.
  • Considering networks as part of embodied organisms within populations.

Main Results:

  • ALNNs facilitate research on language emergence and transmission.
  • The approach supports investigation into language use and its cognitive functions.
  • Language is reframed as a populational, rather than solely individual, phenomenon.

Conclusions:

  • An Artificial Life approach necessitates a revised research agenda for language.
  • ALNNs provide a framework for studying language in more realistic, dynamic contexts.
  • This paradigm shift is crucial for a comprehensive understanding of language's role in cognition and evolution.

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