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Record W2133950214 · doi:10.1093/geronb/gbq039

Lexical Neighborhood Density Effects on Spoken Word Recognition and Production in Healthy Aging

2010· article· en· W2133950214 on OpenAlexaff
Vanessa Taler, Galia Aaron, Laura Steinmetz, David B. Pisoni

Bibliographic record

VenueThe Journals of Gerontology Series B · 2010
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Ottawa
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsAudiologyStimulus (psychology)PsychologySentenceSpeech perceptionWord recognitionLexical accessPerceptionSpeech productionSpeech recognitionLinguisticsCognitive psychologyCognitionComputer scienceMedicineNatural language processing

Abstract

fetched live from OpenAlex

We examined the effects of lexical competition and word frequency on spoken word recognition and production in healthy aging. Older (n = 16) and younger adults (n = 21) heard and repeated meaningful English sentences presented in the presence of multitalker babble at two signal-to-noise ratios, +10 and -3 dB. Each sentence contained three keywords of high or low word frequency and phonological neighborhood density (ND). Both participant groups responded less accurately to high- than low-ND stimuli; response latencies (from stimulus offset to response onset) were longer for high- than low-ND sentences, whereas response durations-time from response onset to response offset-were longer for low- than high-ND stimuli. ND effects were strongest for older adults in the most difficult conditions, and ND effects in accuracy were related to inhibitory function. The results suggest that the sentence repetition task described here taps the effects of lexical competition in both perception and production and that these effects are similar across the life span, but that accuracy in the lexical discrimination process is affected by declining inhibitory function in older adults.

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)0.002
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 score0.746
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.068
GPT teacher head0.328
Teacher spread0.260 · 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

Citations92
Published2010
Admission routes1
Has abstractyes

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Same venueThe Journals of Gerontology Series BSame topicHearing Loss and RehabilitationFrench-language works237,207