Emotional Prosody in Interjections: A Case of Non-arbitrariness in Language
Bibliographic record
Abstract
Emotional prosody shows a connection between meaning and expression, and constitutes a special case of non-arbitrariness in language. Emotional interjections are learned at an early age, and also have a biological basis for their expression. It is therefore possible that emotional prosody of interjections is part of the phonological and semantic representation in the mental lexicon, and hence can be expected to influence visual lexical decisions. If confirmed, this would have some bearing on the debate concerning the relations between ‘linguistic’ and ‘affective’ prosody. A priming experiment was performed on Swedish interjections with two emotional meanings: HAPPINESS (positive words) and DISGUST (negative words). The main question was whether emotional prosody primes written interjections with emotional content, through cross-modal priming, and the chosen method was to elicit lexical decisions in a cross-modal priming task and in isolation. The results show that there was an effect of priming, and that the effect was significantly greater for HAPPINESS words (and HAPPINESS prosody) than for DISGUST words (and DISGUST prosody). For individual words, there was a positive correlation between a high priming effect for the corresponding emotion and the degree of correct interpretations of emotional primes. Furthermore, there was a tendency for high-frequency words to be primed more than low-frequency words, when the emotion of the prosody was matched. There was no such effect for high-frequency words when the emotion of the prosody was mismatched. There was also a tendency to a negative correlation between degree of correct interpretations of emotional primes and high priming effect, when the prosody was mismatched. We interpret these results to mean that it is problematic to regard emotional prosody as non-linguistic and disconnected from the lexicon, since there was a gradual connection between spoken emotional prosody, written emotional interjections, and lexical frequency of interjections.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".