Emotional speech processing: Disentangling the effects of prosody and semantic cues
Bibliographic record
Abstract
To inform how emotions in speech are implicitly processed and registered in memory, we compared how emotional prosody, emotional semantics, and both cues in tandem prime decisions about conjoined emotional faces. Fifty-two participants rendered facial affect decisions (Pell, 2005a), indicating whether a target face represented an emotion (happiness or sadness) or not (a facial grimace), after passively listening to happy, sad, or neutral prime utterances. Emotional information from primes was conveyed by: (1) prosody only; (2) semantic cues only; or (3) combined prosody and semantic cues. Results indicated that prosody, semantics, and combined prosody-semantic cues facilitate emotional decisions about target faces in an emotion-congruent manner. However, the magnitude of priming did not vary across tasks. Our findings highlight that emotional meanings of prosody and semantic cues are systematically registered during speech processing, but with similar effects on associative knowledge about emotions, which is presumably shared by prosody, semantics, and faces.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".