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Record W1868217980 · doi:10.1111/nyas.12617

Preservation of musical memory and engagement in healthy aging and Alzheimer's disease

2015· article· en· W1868217980 on OpenAlexafffund
Lola L. Cuddy, Ritu Sikka, Ashley D. Vanstone

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2015
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaGRAMMY Foundation
KeywordsPsychologyMusicalAutobiographical memoryEpisodic memoryCognitionCognitive psychologyDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

In striking contrast to the difficulties with new learning and episodic memories in aging and especially in Alzheimer's disease (AD), musical long-term memories appear to be largely preserved. Evidence for spared musical memories in aging and AD is reviewed here. New data involve the development of a Musical Engagement Questionnaire especially designed for use with AD patients. The questionnaire assesses behavioral responses to music and is answered by the care partner. Current results show that, despite cognitive loss, persons with mild to moderate AD preserve musical engagement and music seeking. Familiar music evokes personal autobiographical memories for healthy younger and older adults as well and for those with mild to moderate AD. It is argued that music is a prime candidate for being a stimulus for cognitive stimulation because musical memories and associated emotions may be readily evoked; that is, they are strong and do not need to be repaired. Working with and through music as a resource may enhance social and communication functions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.344
GPT teacher head0.449
Teacher spread0.106 · 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 source (direct Gemma or distilled Codex), 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

Citations102
Published2015
Admission routes2
Has abstractyes

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