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Record W2073616800 · doi:10.1037/a0020276

Effects of aging and divided attention on memory for items and their contexts.

2010· article· en· W2073616800 on OpenAlexafffund
Fergus I. M. Craik, Lin Luo, Yuiko Sakuta

Bibliographic record

VenuePsychology and Aging · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalYork UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyContext (archaeology)Cognitive psychologyDevelopmental psychologyCognitionContent-addressable memoryCognitive agingAssociative propertyWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

It is commonly found that memory for context declines disproportionately with aging, arguably due to a general age-related deficit in associative memory processes. One possible mechanism for such deficits is an age-related reduction in available processing resources. In two experiments we compared the effects of aging to the effects of division of attention in younger adults on memory for items and context. Using a technique proposed by Craik (1989), linear functions relating memory performance for items and their contexts were derived for a Young Full Attention group, a Young Divided Attention group, and an Older Adult group. Results suggested that the Old group showed an additional deficit in associative memory that was not mimicked by divided attention. It is speculated that both divided attention and aging are associated with a loss of available processing resources that may reflect inefficient frontal lobe functioning, whereas the additional age-related decrement in associative memory may reflect inefficient processing in medial-temporal regions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.025
GPT teacher head0.322
Teacher spread0.297 · 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

Citations117
Published2010
Admission routes2
Has abstractyes

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