The Impacts of Aging on Affective Prosody Comprehension: A Comparative Study
Bibliographic record
Abstract
PURPOSE: The comprehension of emotions through speech, known as affective prosody comprehension, is an ability that decreases with healthy aging. Affective prosody comprehension is underpinned by three cognitive components (perceptual, lexical, and semantic). However, no data indicate which one(s) is/are impacted by aging. Affective prosody comprehension is based on the analysis of the emotional state of our interlocutor. However, it is still unknown if psycholinguistic variables permitting to access this emotional state such as emotion category, valence, or arousal impact affective prosody comprehension abilities differently according to age. This study aims to investigate the impacts of aging on affective prosody comprehension abilities, exploring the links with the underlying cognitive components and psycholinguistic variables. METHOD: Sixty healthy adults were recruited: 30 younger (18-35 years old) and 30 older individuals (63+ years old). Participants completed a general task of affective prosody comprehension and three specific tasks each evaluating an underlying cognitive component (perceptual, lexical, semantic). RESULTS: Older adults showed a decreased performance in general affective prosody comprehension abilities and in lexical abilities specifically in comparison with younger adults. Also, psycholinguistic variables such as emotion category and arousal played a role in the decreased performance of older adults. CONCLUSION: These results constitute an additional advancement in understanding the normal functioning of affective prosody comprehension processes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29330603.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".