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Record W2910546642 · doi:10.1101/522375

The effects of aging on neural signatures of temporal regularity processing in sounds

2019· preprint· en· W2910546642 on OpenAlexafffund
Björn Herrmann, Chad Buckland, Ingrid S. Johnsrude

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsAuditory cortexAudiologyPsychologyTemporal cortexModulation (music)NeurosciencePerceptionNeural adaptationAmplitude modulationResponsivityElectroencephalographyCortex (anatomy)Frequency modulationAdaptation (eye)PhysicsMedicineComputer scienceAcousticsTelecommunications

Abstract

fetched live from OpenAlex

Abstract Sensitivity to temporal regularity (e.g., amplitude modulation) is crucial for speech perception. Degradation of the auditory periphery due to aging and hearing loss may lead to an increased response gain in auditory cortex, with potential consequences for the processing of temporal regularities. We used electroencephalography recorded from younger (18–33 years) and older (55–80 years) adults to investigate how aging affects neural gain and the neural sensitivity to amplitude modulation in sounds. Aging was associated with reduced adaptation in auditory cortex, suggesting an age-related gain increase. Consistently, neural synchronization in auditory cortex to a 4-Hz amplitude modulation of a narrow-band noise was enhanced in ~30% of older individuals. Despite enhanced responsivity in auditory cortex, sustained neural activity (likely involving auditory and higher-order brain regions) in response to amplitude modulation was absent in older people. Hence, aging may lead to an over-responsivity to amplitude modulation in auditory cortex, but to a diminished regularity representation in higher-order areas.

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: none
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicHearing Loss and Rehabilitation→French-language works237,207→