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Record W2325258169 · doi:10.1037/a0033263

Does aging affect recall more than recognition memory?

2013· article· en· W2325258169 on OpenAlexafffund
Stacey L. Danckert, Fergus I. M. Craik

Bibliographic record

VenuePsychology and Aging · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRecallPsychologyAffect (linguistics)CognitionRecognition memoryCognitive psychologyMemoriaDevelopmental psychologyCognitive agingNeuroscienceCommunication

Abstract

fetched live from OpenAlex

Although it is generally agreed that recall performance declines more than recognition memory performance in the course of normal aging, there are some dissenting voices. There are also a few empirical findings that cast doubt on that conclusion. In light of these ambiguities the present experiments were designed to answer the question in a more definitive fashion. Over a series of 3 experiments, groups of younger and older adults performed recall and recognition tests successively on the same lists of words. Several analyses of the resulting data converge on the conclusion that there is a consistent age-related decrement in recall that is disproportionately greater than the age-related decrement in recognition. This conclusion is in line with several theoretical accounts of age-related differences in cognitive processing and also with emerging evidence from cognitive neuroscience.

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)0.002
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.000
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 head0.047
GPT teacher head0.335
Teacher spread0.288 · 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

Citations127
Published2013
Admission routes2
Has abstractyes

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