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Record W2113823362 · doi:10.1093/geronb/gbs074

Interference From Previous Distraction Disrupts Older Adults' Memory

2012· article· en· W2113823362 on OpenAlexafffund
Renée K. Biss, Karen L. Campbell, Lynn Hasher

Bibliographic record

VenueThe Journals of Gerontology Series B · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDistractionPsychologyCognitive psychologyInterference (communication)Developmental psychologyComputer scienceComputer network

Abstract

fetched live from OpenAlex

OBJECTIVES: Previously relevant information can disrupt the ability of older adults to remember new information. Here, the researchers examined whether prior irrelevant information, or distraction, can also interfere with older adults' memory for new information. METHOD: Younger and older adults first completed a 1-back task on pictures that were superimposed with distracting words. After a delay, participants learned picture-word paired associates and memory was tested using picture-cued recall. In 1 condition (high interference), some pairs included pictures from the 1-back task now paired with new words. In a low-interference condition, the transfer list used all new items. RESULTS: Older adults had substantially lower cued-recall performance in the high- compared with the low-interference condition. In contrast, younger adults' performance did not vary across conditions. DISCUSSION: These findings suggest that even never-relevant information from the past can disrupt older adults' memory for new associations.

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)0.003
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.001
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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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

Citations56
Published2012
Admission routes2
Has abstractyes

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