Tinnitus affects the relative roles of semantics and prosody in the perception of emotions in spoken language
Bibliographic record
Abstract
Objective: Understanding communication difficulties related to tinnitus, by identifying tinnitus-related differences in the perception of spoken emotions, focussing on the roles of semantics (words), prosody (tone of speech) and their interaction.Study sample and design: Twenty-two people-with-tinnitus (PwT) and 24 people-without-tinnitus (PnT) listened to spoken sentences made of different combinations of four discrete emotions (anger, happiness, sadness, neutral) presented in the prosody and semantics (Test for Rating Emotions in Speech). In separate blocks, listeners were asked to attend to the sentence as a whole, integrating both speech channels (gauging integration), or to focus on one channel only (gauging identification and selective attention). Their task was to rate how much they agree the sentence conveys each of the predefined emotions.Results: Both groups identified emotions similarly, and performed with similar failures of selective attention. Group differences were found in the integration of channels. PnT showed a bias towards prosody, whereas PwT weighed both channels equally.Conclusions: Tinnitus appears to impact the integration of the prosodic and semantic channels. Three possible sources are suggested: (a) sensory: tinnitus may reduce prosodic cues. (b) Cognitive: tinnitus-related reduction in cognitive processing. (c) Affective: group differences were related to the existence of tinnitus, but not to the extent of tinnitus complaints and/or affective symptoms.
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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 | 0.003 |
| 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".