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Record W4235063791 · doi:10.31234/osf.io/zucr3

Age differences in the precision of memory at short and long delays

2019· preprint· en· W4235063791 on OpenAlexaff
Stephen Rhodes, Emily E Abenne, Ashley M Meierhofer, Moshe Naveh‐Benjamin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest Hospital
FundersEconomic and Social Research Council
KeywordsRecallPsychologyRecall testTask (project management)Free recallTest (biology)Cognitive psychologySerial position effectObject (grammar)Developmental psychologyAudiologyComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Age differences are well established for many memory tasks assessing both short-term and long-term memory. However, how age differences in performance vary with increasing delay between study and test is less clear. Here we report two experiments in which participants studied a continuous sequence of object-location pairings. Test events were intermixed such that participants were asked to recall the precise location of an object following a variable delay. Older adults exhibit a greater degree of error (distance between studied and recalled locations) relative to younger adults at short (0-2 intervening events) and longer delays (10-25 intervening events). Mixture modeling of the distribution of recall error suggests that older adults do not fail to recall information at a significantly higher rate than younger adults. Instead, what they do recall appears to be less precise. Follow up analyses demonstrate that this age difference emerges following only one or two intervening events between study and test. These findings are consistent with the suggestion that aging does not greatly impair recall from the focus of attention but age differences emerge once information is displaced from this highly accessible state. Further, we suggest that age differences in the precision of memory, but not the probability of successful recall, may be due to the use of more gist-like representations in this task.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.307
Teacher spread0.224 · 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 teacher head, 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

Citations3
Published2019
Admission routes1
Has abstractyes

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