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Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
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Professional or Amateur? The Phonological Output Buffer as a Working Memory Operator.

Neta Haluts1, Massimiliano Trippa2, Naama Friedmann1

  • 1Language and Brain Lab, Sagol School of Neuroscience and School of Education, Tel Aviv University, Tel Aviv-Yafo 69978, Israel.

Entropy (Basel, Switzerland)
|December 8, 2020
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Summary

The Phonological Output Buffer (POB) stores and assembles speech sounds. This study models the POB using a neural network to understand language production and errors in spoken and signed languages.

Keywords:
Potts networkcortexlatching dynamicsphonological output bufferworking memory

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Area of Science:

  • Cognitive Neuroscience
  • Psycholinguistics
  • Computational Neuroscience

Background:

  • The Phonological Output Buffer (POB) is crucial for language production, holding and assembling phonemes into words.
  • Its neural basis remains elusive despite extensive phenomenological data on POB impairments.

Purpose of the Study:

  • To investigate the neural implementation of the POB.
  • To explore whether a single network model can account for POB functions in both spoken and signed language production.

Main Methods:

  • Analysis of phonological error patterns in individuals with POB impairments.
  • Modeling the POB using an autoassociative Potts network to simulate memory storage and unit representation.
  • Testing the network's ability to represent both spoken word units and sign language units.

Main Results:

  • POB impairments exhibit specific error types (omissions, substitutions) affecting various linguistic units (phonemes, words, affixes).
  • Evidence suggests units may be stored in distinct sub-units within the POB.
  • Similar impairments in sign language production raise questions about a shared or distinct buffer mechanism.

Conclusions:

  • A single, extended POB network, connected to a lexicon network, could potentially explain observed error patterns and functions across spoken and signed languages.
  • The autoassociative Potts network model offers a testable framework for understanding POB's distributed neural representation.