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Record W2099609783 · doi:10.1093/geronb/gbs096

Diminished But Not Forgotten: Effects of Aging on Magnitude of Spacing Effect Benefits

2012· article· en· W2099609783 on OpenAlexaff
Patricia M. Simone, Matthew C. Bell, Nicholas J. Cepeda

Bibliographic record

VenueThe Journals of Gerontology Series B · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental HealthYork University
FundersAcademy of FinlandSanta Clara University
KeywordsMagnitude (astronomy)PsychologyAudiologyEpisodic memoryDevelopmental psychologyCognitive psychologyMedicineCognitionNeurosciencePhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: Age-related changes in memory performance are common in paired associate episodic memory tasks, although the deficit can be ameliorated with distributed practice. Benefits of learning episode spacing in older adults have been shown in single-session studies with spaced presentations of items followed by a test. This study examined the magnitude of the spacing effect benefit in older adults relative to younger adults when given a multiday spacing effect paradigm. METHOD: We examined the impact of spacing gap (~15min vs. 24hr) in younger (N = 51, Mage = 19 years, SD = 0.6) and older (N = 54, Mage = 65 years, SD = 8.8) adults with a 10-day retention interval. RESULTS: Spacing of learning episodes benefited both younger and older adults. There was an age-related difference in the magnitude of this benefit that has not been observed in earlier studies. DISCUSSION: These results suggest that spacing benefited the long-term memory of older adults, however the effect was diminished and qualitatively different from that of younger adults.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.320
Teacher spread0.261 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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