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Record W3004920021 · doi:10.1080/13825585.2020.1724866

Younger and older adults’ prospective memory: the role of delay task difficulty

2020· article· en· W3004920021 on OpenAlexaff
Alison M. O’Connor, Karen L. Campbell, Caitlin E. V. Mahy

Bibliographic record

VenueAging Neuropsychology and Cognition · 2020
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsBrock University
Fundersnot available
KeywordsProspective memoryTask (project management)Retrospective memoryPsychologyDevelopmental psychologyYoung adultAudiologyAge groupsCognitionCognitive psychologyEpisodic memoryMedicineDemographyPsychiatryExplicit memory

Abstract

fetched live from OpenAlex

There is mixed evidence on the impact of delay task difficulty on prospective memory (PM) performance and little research has examined this among older adults. The present study examined younger (N = 60) and older (N = 57) adults' prospective memory (PM) performance after completing an easy or difficult Raven's matrices task. To assess whether delay difficulty impacted how often participants thought about their PM intention, participants were asked to report on what they thought about during the delay task itself and retrospectively after all tasks were completed. Younger adults outperformed older adults on the PM task; however, delay task difficulty had no impact PM for either age group. Reports of thinking about the intention during the delay task differed by age group depending whether they were online or retrospective, however, overall greater reports of thinking about the intention was positively associated with PM performance.

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)0.010
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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