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A parallel distributed processing approach to automaticity.

J D Cohen1, D Servan-Schreiber, J L McClelland

  • 1Carnegie Mellon University.

The American Journal of Psychology
|January 1, 1992
PubMed
Summary
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The parallel distributed processing (PDP) framework explains automaticity using activation-based processing and learning. PDP models account for automaticity phenomena and offer an alternative to traditional automatic vs. controlled processing dichotomies.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Traditional theories struggle to explain the nuances of automaticity.
  • Automaticity is often viewed as a dichotomy between automatic and controlled processing.

Purpose of the Study:

  • To explore how parallel distributed processing (PDP) principles can explain automaticity.
  • To provide a computational framework for understanding automatic and controlled processing.

Main Methods:

  • Utilized simulation models based on PDP principles.
  • Applied principles of graded, activation-based processing, incremental learning, and interactive processing.
  • Modeled performance in the Stroop color-word and Eriksen response-competition tasks.

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Main Results:

  • PDP models successfully account for major automaticity phenomena.
  • The framework offers an alternative to the automatic/controlled processing dichotomy.
  • Demonstrated how attention modulates processing and how processing influences attention.

Conclusions:

  • The PDP framework provides a robust explanation for automaticity.
  • It reconciles seemingly automatic processes with attentional influences.
  • Highlights the bidirectional relationship between attention and processing.