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Record W2019215542 · doi:10.1002/acp.770

Virtual week and actual week: Age‐related differences in prospective memory

2000· article· en· W2019215542 on OpenAlexafffund
Peter G. Rendell, Fergus I. M. Craik

Bibliographic record

VenueApplied Cognitive Psychology · 2000
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsUniversity of Toronto
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaAustralian Catholic University
KeywordsProspective memoryPsychologyTask (project management)Young adultDevelopmental psychologyAge groupsCognitive psychologyCognitionDemographyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Several previous studies have shown that whereas young adults perform better than older adults on prospective memory (PM) tasks in the laboratory, this superiority is often reversed in real‐life PM tasks. The present studies investigated this paradox by creating a laboratory task in the form of a board game (Virtual Week) that mimicked many features of daily living. It was hypothesized that older adults might use strategies derived from their more structured lives to outperform young adults on the board game. However, contrary to our prediction, it was found that younger adults were superior. In Experiment 2 we had participants perform very similar PM tasks in real life (Actual Week), and found that now the older adults were generally superior to their younger counterparts. Possible reasons are discussed for this striking age‐related difference between laboratory‐based and naturalistic PM tasks. Copyright © 2000 John Wiley & Sons, Ltd.

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)0.005
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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

Citations359
Published2000
Admission routes2
Has abstractyes

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