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Record W2946518408 · doi:10.1097/wnr.0000000000001267

The impact of aging on neurophysiological entrainment to a metronome

2019· article· en· W2946518408 on OpenAlexaff
Sarah A. Sauvé, Emily Bolt, David Fleming, Benjamin Rich Zendel

Bibliographic record

VenueNeuroreport · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMetronomeStimulus (psychology)Entrainment (biomusicology)RhythmAudiologyPsychologyNeurophysiologyNeuroscienceCognitionElectroencephalographyCognitive psychologyMedicinePhysicsAcoustics

Abstract

fetched live from OpenAlex

In music, entrainment to the beat allows listeners to make predictions about upcoming events. Previous work has shown that neural oscillations will entrain to the beat of the music or rhythmic stimuli. Despite the fact that aging is known to impact both auditory and cognitive processing, little is known about how aging affects neural entrainment to rhythmic stimuli. In this study, younger and older participants listened to isochronous sequences at a slower and faster rate while EEG data was recorded. Steady-state evoked potentials had amplitude peaks at the stimulus rate and its harmonics. Steady-state evoked potentials at the stimulus rate and the first harmonic was attenuated in older adults compared to younger adults. Additionally, no amplitude difference was found for the second and third harmonics in older adults, while there was a decrease in amplitude in younger adults. This age-related decline in the entrainment specificity of the brain responses to the stimulus rate, suggests that aging may decrease the ability to entrain to stimuli in the environment, and further suggests that older adults may be less able to inhibit neural entrainment that is not directly related to the incoming stimulus.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.307

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 science0.0000.000
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 head0.042
GPT teacher head0.327
Teacher spread0.285 · 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 designBench or experimental
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

Citations17
Published2019
Admission routes1
Has abstractyes

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