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Record W4398138273 · doi:10.1080/02699931.2024.2351960

Age-related differences in processing of emotions in speech disappear with babble noise in the background

2024· article· en· W4398138273 on OpenAlexaff
Yehuda I. Dor, Daniel Algom, Vered Shakuf, Boaz M. Ben‐David

Bibliographic record

VenueCognition & Emotion · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersIsrael Science Foundation
KeywordsProsodyPsychologyEmotional prosodyCognitionNoise (video)Semantics (computer science)Speech perceptionCognitive psychologyAudiologyPerceptionSpeech recognitionComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Older adults process emotional speech differently than young adults, relying less on prosody (tone) relative to semantics (words). This study aimed to elucidate the mechanisms underlying these age-related differences via an emotional speech-in-noise test. A sample of 51 young and 47 older adults rated spoken sentences with emotional content on both prosody and semantics, presented on the background of wideband speech-spectrum noise (sensory interference) or on the background of multi-talker babble (sensory/cognitive interference). The presence of wideband noise eliminated age-related differences in semantics but not in prosody when processing emotional speech. Conversely, the presence of babble resulted in the elimination of age-related differences across all measures. The results suggest that both sensory and cognitive-linguistic factors contribute to age-related changes in emotional speech processing. Because real world conditions typically involve noisy background, our results highlight the importance of testing under such conditions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.297
Teacher spread0.238 · 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 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

Citations11
Published2024
Admission routes1
Has abstractyes

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