An Instructable Connectionist/Control Architecture: Using Rule-Based Instructions to Accomplish Connectionist Learning in a Human Time Scale

Abstract

We describe a hybrid cognitive architecture that combines connectionist and controlled processing. The connectionist/control architecture (CAP2) uses instructions to decompose cognitive tasks into subtasks that can be learned in a human time scale. A CAP2 simulation model that uses the same task decomposition used by human subjects learns a logic task ten times faster than a standard connectionist model that does not use task decomposition. Rules for carrying out tasks are stored in a sequential network (Elman, 1988; Jordan, 1986) that controls the flow of information through a modular connectionist network. We argue that the CAP2 architecture better matches the human cognitive architectures than purely symbolic or purely connectionist architecture. Keywords: Human learning, Connectionist models, Automatic processing, Controlled processing, Cognitive architecture.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA219274

Entities

People

  • Walter Schneider
  • William L. Oliver

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Brain
  • Coding
  • Cognition
  • Cognitive Science
  • Complex Systems
  • Computational Science
  • Computer Programming
  • Computer Science
  • Computers
  • Instructors
  • Logic Gates
  • Network Architecture
  • Neural Networks
  • Psychology
  • Simulations

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.