A Model of the Phonological Loop: Generalization and Binding

Abstract

We present a neural network model that shows how the prefrontal cortex, interacting with the basal ganglia, can maintain a sequence of phonological information in activation-based working memory (i.e., the phonological loop). The primary function of this phonological loop may be to transiently encode arbitrary bindings of information necessary for tasks--the combinatorial expressive power of language enables very flexible binding of essentially arbitrary pieces of information. Our model takes advantage of the close-class nature of phonemes, which allows different neural representations of all possible phonemes at each sequential position to be encoded. To make this work, we suggest that the basal ganglia provide a region-specific update signal that allocates phonemes to the appropriate sequential coding slot. To demonstrate that flexible, arbitrary binding of novel sequences can be supported by this mechanism, we show that the model can generalize to novel sequences after moderate amounts of training.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA496081

Entities

People

  • Randall C. O'Reilly
  • Rodolfo Soto

Organizations

  • University of Colorado Boulder

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Brain
  • Computer Programming
  • Formal Languages
  • Information Operations
  • Language
  • Neural Networks
  • Sequences
  • Training

Readers

  • Artificial Intelligence
  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks