The impacts of aging on the comprehension of affective prosody: A systematic review
Bibliographic record
Abstract
Recent clinical reports have suggested a possible decline in the ability to understand emotions in speech (affective prosody comprehension) with aging. The present study aims to further examine the differences in performance between older and younger adults in terms of affective prosody comprehension. Following a recent cognitive model dividing affective prosody comprehension into perceptual and lexico-semantic components, a cognitive approach targeting these components was adopted. The influence of emotions' valence and category on aging performance was also investigated. A systematic review of the literature was carried out using six databases. Twenty-one articles, presenting 25 experiments, were included. All experiments analyzed affective prosody comprehension performance of older versus younger adults. The results confirmed that older adults' performance in identifying emotions in speech was reduced compared to younger adults. The results also brought out the fact that affective prosody comprehension abilities could be modulated by the emotion category but not by the emotional valence. Various theories account for this difference in performance, namely auditory perception, brain aging, and socioemotional selectivity theory suggesting that older people tend to neglect negative emotions. However, the explanation of the underlying deficits of the affective prosody decline is still limited.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".