Categorizing Emotion in Spoken Language: An Analysis of Semantic and Prosodic Contributions to Emotional Communication
Bibliographic record
Abstract
The current study aimed to replicate and expand upon research conducted by Bagley, Abramowitz and Kosson (2009) to examine categorization of emotional sentences among non-psychopathic individuals.36 monolingual English-speaking undergraduate participants categorized spoken English sentences (produced with neutral prosody but containing semantic cues to emotion) and French sentences (produced with appropriate prosody but with no semantic cues to emotion) into one of five emotion categories: happiness, sadness, anger, fear, or neutral.By isolating the semantic and prosodic information available to listeners, we determined that categorization accuracy was higher among sentences expressing anger in the prosodic condition.Accuracy was higher among sentences expressing all other emotions in the semantic condition.Overall, the lowest categorization accuracy was found for sentences expressing fear in the prosodic condition.Across all emotion categories and both presentation conditions, reaction time was longest for sentences expressing fear in the prosodic condition.Although all participants in the current study had normative scores on the Self-Report Psychopathy Scale, those with relatively high scores displayed lower categorization accuracy for semantic sentences expressing happiness, anger and fear than lower-scoring participants.An extension of the current study comparing this normative sample to a group of individuals with psychopathy will need to account for possible implications of subclinical psychopathic characteristics on vocal affect categorization accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".