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Record W3093607121 · doi:10.3390/brainsci10110770

Semantic Processing in Healthy Aging and Alzheimer’s Disease: A Systematic Review of the N400 Differences

2020· review· en· W3093607121 on OpenAlexaff
Marilyne Joyal, Charles Groleau, Clara Bouchard, Maximiliano A. Wilson, Shirley Fecteau

Bibliographic record

VenueBrain Sciences · 2020
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité LavalCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsN400Semantic memoryPsychologyHealthy agingPathologicalDiseaseAssociation (psychology)CognitionEvent-related potentialAudiologyCognitive psychologyDevelopmental psychologyNeuroscienceMedicineGerontologyPathology

Abstract

fetched live from OpenAlex

Semantic deficits are common in individuals with Alzheimer's disease (AD). These deficits notably impact the ability to understand words. In healthy aging, semantic knowledge increases but semantic processing (i.e., the ability to use this knowledge) may be impaired. This systematic review aimed to investigate semantic processing in healthy aging and AD through behavioral responses and the N400 brain event-related potential. The results of the quantitative and qualitative analyses suggested an overall decrease in accuracy and increase in response times in healthy elderly as compared to young adults, as well as in individuals with AD as compared to age-matched controls. The influence of semantic association, as measured by N400 effect amplitudes, appears smaller in healthy aging and even more so in AD patients. Thus, semantic processing differences may occur in both healthy and pathological aging. The establishment of norms of healthy aging for these outcomes that vary between normal and pathological aging could eventually help early detection of AD.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.035
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.130
GPT teacher head0.397
Teacher spread0.267 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations43
Published2020
Admission routes1
Has abstractyes

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