Literally or prosodically? Recognising emotional discourse in alexithymia
Bibliographic record
Abstract
Alexithymia is characterised by difficulties in identifying, recognising, and describing emotions. We studied alexithymia in the context of speech comprehension, specifically investigating the incongruent condition between prosody and the literal meaning of words in emotion-based discourse. In two experiments, participants were categorised as having high or low alexithymia scores based on the TAS-20 scale and listened to three-sentence narratives where the emotional prosody of a key phrase or a keyword was congruent or incongruent with its literal meaning. The incongruent condition resulted in slower reaction times and lower accuracy in recognition of emotions. This incongruence effect was also evident for individuals with high alexithymia, except for anger. They recognised anger as accurately in both congruent and incongruent conditions. Contrary to our hypothesis, however, individuals with high alexithymia did not show an overall difference in emotion recognition compared to the low alexithymia group. These findings highlight the nuanced relationship between emotional prosody and literal meaning, offering insights into how individuals with varying levels of alexithymia process emotional discourse. Understanding these dynamics has implications for both cognitive research and clinical practice, providing valuable perspectives on speech comprehension, especially in situations involving incongruence between prosody and word meaning.
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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 | 0.006 |
| 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) | 0.002 | 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".