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Record W4401892440 · doi:10.1177/09637214241263020

Hyper-Binding: Older Adults Form Too Many Associations, Not Too Few

2024· article· en· W4401892440 on OpenAlexafffund
Karen L. Campbell, Emily E. Davis

Bibliographic record

VenueCurrent Directions in Psychological Science · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBrock University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsForgettingPsychologyDevelopmental psychologyAssociation (psychology)Content-addressable memoryEpisodic memoryCognitive psychologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Associative memory declines with age, and this decline is thought to stem from a decreased ability to form new associations or bind information together. However, a growing body of work suggests that (a) the binding process itself remains relatively intact with age when tested implicitly and (b) older adults form excessive associations (or "hyper-bind") because of a decreased ability to control attention. In this article, we review evidence for the hyper-binding hypothesis. This work shows that older adults form more nontarget associations than younger adults, which leads to increased interference at retrieval and forgetting, an effect that may extend to others with poor attentional control, such as children and people with attention-deficit disorder. We discuss why hyper-binding is apparent only under implicit test conditions and how it affects memory for everyday events. Although hyper-binding likely contributes to forgetting, it may also confer certain advantages when seemingly irrelevant associations later become relevant.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.098
GPT teacher head0.422
Teacher spread0.324 · 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 designOther design
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

Citations6
Published2024
Admission routes2
Has abstractyes

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