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Encoding and Accessing Linguistic Representations in a Dynamically Structured Holographic Memory System.

Dan Parker1, Daniel Lantz1

  • 1Department of English, Linguistics Program, College of William & Mary.

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Summary
This summary is machine-generated.

This study integrates a holographic memory system into the ACT-R cognitive architecture to improve sentence comprehension models. The new model better explains human error rates and response latencies, particularly for sentences with negative polarity items.

Keywords:
ACT-RBindingHolographic reduced representationsLanguage processingMemoryNegative polarity

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Cognitive Architecture

Background:

  • The ACT-R cognitive architecture models sentence comprehension through working memory retrievals.
  • Existing ACT-R models sometimes over-predict retrieval interference, failing to explain certain linguistic phenomena.
  • Negative polarity items (NPIs) present a challenge for current models of sentence processing.

Purpose of the Study:

  • To develop a computational model that enhances the ACT-R cognitive architecture.
  • To investigate the role of a dynamically structured holographic memory system in linguistic representation.
  • To explain previously unexplained effects in sentence comprehension, particularly with NPIs.

Main Methods:

  • Integrating a dynamically structured holographic memory system into the ACT-R cognitive architecture.
  • Replacing ACT-R's standard declarative memory with the holographic memory system.
  • Comparing model predictions with behavioral data, including reading times, grammaticality judgments, error rates, and response latencies.

Main Results:

  • The integrated model explains a wider range of behavioral data compared to the original ACT-R model.
  • The holographic memory system provides a cognitively plausible explanation for NPI processing.
  • The enhanced model shows a better fit to human error rates and response latencies.

Conclusions:

  • A dynamically structured holographic memory system can improve computational models of sentence comprehension.
  • Unifying holographic memory with ACT-R offers a more comprehensive framework for understanding linguistic representation and access.
  • This proof-of-concept demonstrates the potential for integrating independent computational cognitive frameworks.