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Reconciling Two Computational Models of Working Memory in Aging.

Violette Hoareau1,2, Benoît Lemaire1,2, Sophie Portrat1,2

  • 1Laboratory of Psychology and Neurocognition, University Grenoble Alpes, France.

Topics in Cognitive Science
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Summary

Older adults struggle with working memory due to difficulties in processing information during breaks and increased item confusion, not weaker initial encoding. Computational models highlight these age-related working memory deficits.

Keywords:
AgingComputational modelingDecayInterferenceSOB-CSTBRSWorking memory

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Neuroscience

Background:

  • Working memory (WM) performance declines with age.
  • Two computational models, TBRS* and SOB-CS, explain forgetting via time-based decay and interference, respectively, based on young adult data.

Purpose of the Study:

  • To evaluate the applicability of TBRS* and SOB-CS models to older adults' working memory performance.
  • To identify the specific factors contributing to age-related working memory decline.

Main Methods:

  • Application of TBRS* and SOB-CS computational models to complex span task data from young and older adults.
  • Analysis of model parameters to investigate the sources of age-related performance differences.

Main Results:

  • Both TBRS* and SOB-CS models failed to accurately predict older adults' working memory performance.
  • Older adults' poorer performance was linked to difficulties during post-distractor processing intervals and increased item confusion, rather than encoding deficits.

Conclusions:

  • Current computational models of working memory are insufficient to explain age-related changes.
  • Age-related working memory decline is associated with impaired consolidation and increased interference, not solely with initial memory encoding limitations.