Age-Related Differences in the Perception of Emotion in Spoken Language: The Relative Roles of Prosody and Semantics
Bibliographic record
Abstract
Purpose We aim to identify the possible sources for age-related differences in the perception of emotion in speech, focusing on the distinct roles of semantics (words) and prosody (tone of speech) and their interaction. Method We implement the Test for Rating of Emotions in Speech ( Ben-David, Multani, Shakuf, Rudzicz, & van Lieshout, 2016 ). Forty older and 40 younger adults were presented with spoken sentences made of different combinations of 5 emotional categories (anger, fear, happiness, sadness, and neutral) presented in the prosody and semantics. In separate tasks, listeners were asked to attend to the sentence as a whole, integrating both speech channels, or to focus on 1 channel only (prosody/semantics). Their task was to rate how much they agree the sentence is conveying a predefined emotion. Results (a) Identification of emotions: both age groups identified presented emotions. (b) Failure of selective attention: both age groups were unable to selectively attend to 1 channel when instructed, with slightly larger failures for older adults. (c) Integration of channels: younger adults showed a bias toward prosody, whereas older adults showed a slight bias toward semantics. Conclusions Three possible sources are suggested for age-related differences: (a) underestimation of the emotional content of speech, (b) slightly larger failures to selectively attend to 1 channel, and (c) different weights assigned to the 2 speech channels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".