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Learning and morphological change

M Hare1, J L Elman

  • 1Center for Research in Language, University of California, San Diego, La Jolla 92093-0526, USA.

Cognition
|July 1, 1995
PubMed
Summary

This study models English verb morphology changes using a connectionist approach. It shows how irregular verb forms become regularized over time due to learning patterns in networks.

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Area of Science:

  • Computational linguistics
  • Historical linguistics
  • Cognitive science

Background:

  • English verb morphology has evolved significantly from Old English to Modern English.
  • Understanding the mechanisms of language change, particularly grammatical regularization, is crucial.

Purpose of the Study:

  • To model the historical changes in English verb inflection using a connectionist approach.
  • To investigate how morphological knowledge is acquired and applied in language change.

Main Methods:

  • Developed a technique for modeling historical change in connectionist networks.
  • Trained networks on Old English verb data, simulating learning processes.
  • Used error propagation between networks to mimic generational transmission of linguistic patterns.

Main Results:

  • Connectionist networks demonstrated a tendency to regularize irregular patterns over simulated time.
  • Learned patterns were influenced by frequency and phonological regularities, mirroring historical linguistic trends.
  • Simulated results closely matched observed historical developments in English verb inflection.

Conclusions:

  • Connectionist network dynamics provide a computational rationale for the observed regularization in English verb morphology.
  • The model successfully accounts for the gradual shift from complex Old English verb systems to the simpler Modern English system.
  • This approach offers insights into the cognitive underpinnings of language evolution.

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