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Record W4396646480 · doi:10.31234/osf.io/2h7kv

Differential attentional demands on implicit and explicit associative memory in children 8-12 years old

2024· preprint· en· W4396646480 on OpenAlexaff
Emily E. Davis, Hannah Thomas, Matthew Price, Karen L. Campbell, Caitlin E. V. Mahy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBrock University
Fundersnot available
KeywordsAssociative propertyCognitive psychologyPsychologyImplicit memoryExplicit memoryDifferential (mechanical device)Episodic memoryCognitionNeuroscienceMathematicsPure mathematicsEngineering

Abstract

fetched live from OpenAlex

Associative memory improves during childhood, suggesting an age-related improvement in the binding mechanism responsible for linking information together. However, tasks designed to measure associative memory not only measure binding, but also place demands on attention. This makes it difficult to dissociate age-related improvements in memory from the development of attention. One way to reduce attentional demands is to test memory implicitly versus explicitly. In this study, children (8-, 10-, and 12-years-old) completed separate implicit and explicit associative memory tests. For the implicit task, children incidentally encoded pairs of objects by making an object categorization decision. At test, they completed the same task, but unbeknownst to the participants, the pairs were either intact, rearranged, or new. Next, children completed another incidental encoding phase, then an explicit test in which they indicated whether the pairs were intact, rearranged, or new. For the implicit test, all age groups had faster reaction times for intact than rearranged pairs (indicative of implicit associative memory). In the explicit test, memory performance (d’) improved with age. A separate measure of attention related to performance in both the explicit and implicit tasks. Together, these results support that attentional mechanisms are responsible for age-related improvements in associative memory.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.286
Teacher spread0.259 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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