Age-Related Changes in the Perception of Emotions in Speech: Assessing Thresholds of Prosody and Semantics Recognition in Noise for Young and Older Adults
Bibliographic record
Abstract
Older adults process emotions in speech differently than do young adults. However, it is unclear whether these age-related changes impact all speech channels to the same extent, and whether they originate from a sensory or a cognitive source. The current study adopted a psychophysical approach to directly compare young and older adults' sensory thresholds for emotion recognition in two channels of spoken-emotions: prosody (tone) and semantics (words). A total of 29 young adults and 26 older adults listened to 50 spoken sentences presenting different combinations of emotions across prosody and semantics. They were asked to recognize the prosodic or semantic emotion, in separate tasks. Sentences were presented on the background of speech-spectrum noise ranging from SNR of -15 dB (difficult) to +5 dB (easy). Individual recognition thresholds were calculated (by fitting psychometric functions) separately for prosodic and semantic recognition. Results indicated that: (1). recognition thresholds were better for young over older adults, suggesting an age-related general decrease across channels; (2). recognition thresholds were better for prosody over semantics, suggesting a prosodic advantage; (3). importantly, the prosodic advantage in thresholds did not differ between age groups (thus a sensory source for age-related differences in spoken-emotions processing was not supported); and (4). larger failures of selective attention were found for older adults than for young adults, indicating that older adults experienced larger difficulties in inhibiting irrelevant information. Taken together, results do not support a sole sensory source, but rather an interplay of cognitive and sensory sources for age-related differences in spoken-emotions processing.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".