Roles of bilingualism and musicianship in resisting semantic or prosodic interference while recognizing emotion in sentences
Bibliographic record
Abstract
Abstract Listeners can use the way people speak (prosody) or what people say (semantics) to infer vocal emotions. It can be speculated that bilinguals and musicians can better use the former rather than the latter compared to monolinguals and non-musicians. However, the literature to date has offered mixed evidence for this prosodic bias. Bilinguals and musicians are also arguably known for their ability to ignore distractors and can outperform monolinguals and non-musicians when prosodic and semantic cues conflict. In two online experiments, 1041 young adults listened to sentences with either matching or mismatching semantic and prosodic cues to emotions. 526 participants were asked to identify the emotion using the prosody and 515 using the semantics. In both experiments, performance suffered when cues conflicted, and in such conflicts, musicians outperformed non-musicians among bilinguals, but not among monolinguals. This finding supports an increased ability of bilingual musicians to inhibit irrelevant information in speech.
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 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) | 0.003 | 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".