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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Connectionist perspectives on language learning, representation and processing.

Marc F Joanisse1, James L McClelland2

  • 1Psychology/Brain and Mind Institute, The University of Western Ontario, London, ON, Canada.

Wiley Interdisciplinary Reviews. Cognitive Science
|August 12, 2015
PubMed
Summary
This summary is machine-generated.

Connectionist models offer a new perspective on language, simulating neural networks to understand learning and processing. This approach effectively models both regular and irregular language patterns, challenging traditional grammar-based theories.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Formal linguistics traditionally emphasizes deterministic symbolic grammars.
  • This focus has de-emphasized the roles of learning and statistics in language acquisition and processing.
  • An alternative approach, parallel distributed processing (connectionism), offers a new framework.

Purpose of the Study:

  • To review recent progress in connectionist models of language processing.
  • To demonstrate how connectionist models can capture language learning, representation, and processing.
  • To highlight the ability of connectionist models to account for irregular linguistic patterns.

Main Methods:

  • Simulation of artificial neural networks to model cognitive processes.
  • Application of connectionist frameworks to auditory word recognition, reading, morphology, and syntax.
  • Analysis of how learning and statistical patterns are integrated within these models.

Main Results:

  • Connectionist models successfully capture key aspects of language learning and processing.
  • These models provide insights into the underlying mechanisms of linguistic behavior.
  • Irregular linguistic patterns, often problematic for rule-based systems, are naturally accommodated.

Conclusions:

  • Connectionist approaches offer a powerful alternative to traditional formal grammars in linguistics.
  • Simulating neural networks provides a robust method for understanding language acquisition and processing.
  • This framework unifies the explanation of rule-like and exception-like linguistic phenomena.