Use of affective prosody by young and older adults.
Bibliographic record
Abstract
Emotion is conveyed in speech by semantic content (what is said) and by prosody (how it is said). Prior research suggests that older adults benefit from linguistic prosody when comprehending language but that they have difficulty understanding affective prosody. In a series of 3 experiments, young and older adults listened to sentences in which the emotional cues conveyed by semantic content and affective prosody were either congruent or incongruent and then indicated whether the talker sounded happy or sad. When judging the emotion of the talker, young adults were more attentive to the affective prosodic cues than to the semantic cues, whereas older adults performed less consistently when these cues conflicted. Participants' reading and repetition of the sentences were recorded so that age- and emotion-related changes in the production of emotional speech cues could be examined. Both young and older adults were able to produce affective prosody. The age-related difference in perceiving emotion was eliminated when listeners repeated the sentences before responding, consistent with previous findings regarding the beneficial role of repetition in conversation. The results of these experiments suggest that there are age-related differences in interpreting affective prosody but that repeating may be a compensatory strategy that could minimize the everyday consequences of these differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".