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Record W2966554855 · doi:10.1002/hipo.23142

Recalling the firedog: Individual differences in associative memory for unitized and nonunitized associations among older adults

2019· article· en· W2966554855 on OpenAlexafffund
Talya Sadeh, Christa Dang, Sigal Gat‐Lazer, Morris Moscovitch

Bibliographic record

VenueHippocampus · 2019
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchAzrieli FoundationIsrael Science FoundationAmerican Surgical Association Foundation
KeywordsRecallPsychologyEpisodic memoryCognitive psychologyCognitionAssociative propertyContent-addressable memoryExplicit memoryFree recallHippocampusImplicit memoryDevelopmental psychologyFalse memoryNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Memory deficits in aging are characterized by impaired hippocampus-mediated relational binding-the formation of links between items in memory. By reducing reliance on relational binding, unitization of two items into one concept enhances associative recognition among older adults. Can a similar enhancement be obtained when probing memory with recall? This question has yet to be examined, because recall has been assumed to rely predominantly on relational binding. Inspired by recent evidence challenging this assumption, we investigated individual differences in older adults' recall of unitized and nonunitized associations. Compared with successfully aging individuals, older adults with mild memory deficits, typically mediated by the hippocampus, were impaired in recall of paired-associates in a task which relies on relational binding (study: "PLAY-TUNNEL"; test: PLAY-T?). In stark contrast, the two groups showed similar performance when items were unitized into a novel compound word (study: "LOVEGIGGLE"; test: LOVEG?). Thus, boosting nonrelational aspects of recall enhances associative memory among aging individuals with subtle memory impairments to comparable levels as successfully aging older adults.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.251
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.062
GPT teacher head0.283
Teacher spread0.221 · 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 routes2
Has abstractyes

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