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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
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Related Experiment Video

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Computational Phenotyping of Cognitive Decline With Retest Learning.

Zita Oravecz1,2, Joachim Vandekerckhove3,4, Jonathan G Hakun2,5

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The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
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PubMed
Summary
This summary is machine-generated.

Computational cognitive markers can identify mild cognitive impairment (MCI) by analyzing learning and forgetting patterns. These markers offer sensitive indicators for early detection of cognitive decline and Alzheimer's Disease and Related Dementias (ADRD) risk.

Keywords:
Cognitive psychometricsComputational modelingRetest learningSubtle cognitive decline

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Area of Science:

  • Computational neuroscience
  • Cognitive psychology
  • Gerontology

Background:

  • Cognitive change involves performance gains and decline, necessitating methods to track subtle changes.
  • Identifying risk factors for Alzheimer's Disease and Related Dementias (ADRD) requires disentangling these complex dynamics.
  • Ultra-brief, high-frequency cognitive assessments via smartphones offer novel data collection methods.

Purpose of the Study:

  • To apply a computational cognitive model to analyze learning and forgetting dynamics in cognitive assessment data.
  • To identify cognitive markers that are sensitive to preclinical cognitive change and mild cognitive impairment (MCI).
  • To assess the utility of computational markers in distinguishing MCI from normal aging, independent of age effects.

Main Methods:

  • Utilized data from the Einstein Aging Study (EAS; n=316) including smartphone-based cognitive assessments.
  • Applied a computational model of learning and forgetting to response time data from visual short-term working memory (Color Shapes) and processing speed (Symbol Search) tasks.
  • Extracted markers including intraindividual variability, within-burst learning, asymptotic performance, and forgetting of retest gains.

Main Results:

  • Asymptotic performance correlated with mild cognitive impairment (MCI) and age, with evidence of asymptotic slowing over time.
  • Long-term forgetting, learning rate, and within-person variability were unique indicators of MCI, independent of age.
  • Computational cognitive markers demonstrated sensitivity and specificity in identifying MCI.

Conclusions:

  • Computational cognitive markers show potential as sensitive and specific indicators of preclinical cognitive change.
  • These markers can aid in the early identification of individuals at risk for ADRD.
  • The findings support the use of computational markers for targeted interventions and improved cognitive health monitoring.