The Case for Interactionism in Language Processing.

Abstract

Interactive models of language processing assume that information flows both bottom-up and top-down, so that the representations formed at each level may be influenced by higher as well as lower levels. I describe a framework called the interactive activation framework that embeds this key assumption among others, including the assumption that influences from different sources are combined non-linearly. This non-linearity means that information that may be decisive under some circumstances have little or no effect under other conditions. Two attempts to rule out an interactive account in favor of models in which individual components of the language processing system act autonomously are considered in light of the interactive activation framework. In both cases, the facts are as expected from the principles of interactive activation. In general, existing facts do not rule out an interactive account, but they do not require one either. To demonstrate that more definitive tests of interaction are possible. I describe an experiment that demonstrates a new kind of influence of a higher level factor (lexical membership) a lower level of processing (phoneme identification). The experiment illustrates one reason why feedback from higher levels is computationally desirable; it allows lower levels to be tuned by contextual factors so that they can supply more accurate information to higher levels.

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Document Details

Document Type
Technical Report
Publication Date
Apr 28, 1987
Accession Number
ADA180133

Entities

People

  • James McClelland

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Birds
  • Classification
  • Cognitive Science
  • Computer Science
  • Detectors
  • Identification
  • Information Processing
  • Language
  • Linguistics
  • Materials
  • Psychology
  • Recognition
  • Scientists
  • Security
  • Simulations
  • Universities
  • Word Recognition

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Linguistics
  • Theoretical Analysis.