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Record W7154619161 · doi:10.48448/bse3-jh48

Neural Signatures of Semantic and Perceptual Memory Formation Become More Similar Across Development

2025· other· W7154619161 on OpenAlexaff
Cognitive Science Society 2025, Margaret Schlichting, Sagana Vijayarajah, Alexander W McArthur

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSemantic memoryFunctional magnetic resonance imagingEncoding (memory)RecallPerceptionPrefrontal cortexExplicit memorySimilarity (geometry)Episodic memoryVisual memory

Abstract

fetched live from OpenAlex

In adults, the contribution of the prefrontal cortex and hippocampus to memory encoding varies depending on the type of information being learned. Because these regions are still developing in children, their contribution to the formation of memories for different types of associations may differ from that of adults. Here, we examined how semantic and perceptual similarity between items affects memory behaviour and neural engagement in children (6-7 years) and adults. Participants completed a pair learning task during functional magnetic resonance imaging, in which pairs were perceptually or semantically related. Memory was tested outside the scanner with cued recall. Semantic similarity facilitated recall in both age groups, but more so in adults. Neurally, semantic pairs elicited broad frontoparietal activity while perceptual pairs engaged ventral visual and lateral prefrontal areas. Children showed more distinct neural responses to semantic versus perceptual pairs than adults, as well as more engagement in anterior hippocampus for semantic than perceptual pairs. These findings suggest that semantic similarity provides a powerful scaffold for memory across development, with age-related changes in memory encoding marked by a shift toward reliance on more integrated neural systems.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.009
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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