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Record W2012214524 · doi:10.4061/2011/407074

Practice-Oriented Retest Learning as the Basic Form of Cognitive Plasticity of the Aging Brain

2011· article· en· W2012214524 on OpenAlexafffund
Lixia Yang

Bibliographic record

VenueJournal of Aging Research · 2011
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMemorizationCognitionVariety (cybernetics)Executive functionsCognitive skillPsychologyCognitive agingCognitive psychologyNeuroplasticityEffects of sleep deprivation on cognitive performanceDevelopmental psychologyMedicineNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

It has been well documented that aging is associated with declines in a variety of cognitive functions. A growing body of research shows that the age-related cognitive declines are reversible through cognitive training programs, suggesting maintained cognitive plasticity of the aging brain. Retest learning represents a basic form of cognitive plasticity. It has been consistently demonstrated for adults in young-old and old-old ages. Accumulated research indicates that retest learning is effective, robust, endurable and could occur at a more conceptual level beyond item-specific memorization. Recent studies also demonstrate promisingly broader transfer effects from retest practice of activities involving complex executive functioning to other untrained tasks. The results shed light on the development of self-guided mental exercise programs to improve cognitive performance and efficiency of the aging brain. The relevant studies were reviewed, and the findings were discussed in light of their limitations, implications, and future directions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.431
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2011
Admission routes2
Has abstractyes

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