Fear across the senses: brain responses to music, vocalizations and facial expressions
Bibliographic record
Abstract
Intrinsic emotional expressions such as those communicated by faces and vocalizations have been shown to engage specific brain regions, such as the amygdala. Although music constitutes another powerful means to express emotions, the neural substrates involved in its processing remain poorly understood. In particular, it is unknown whether brain regions typically associated with processing 'biologically relevant' emotional expressions are also recruited by emotional music. To address this question, we conducted an event-related functional magnetic resonance imaging study in 47 healthy volunteers in which we directly compared responses to basic emotions (fear, sadness and happiness, as well as neutral) expressed through faces, non-linguistic vocalizations and short novel musical excerpts. Our results confirmed the importance of fear in emotional communication, as revealed by significant blood oxygen level-dependent signal increased in a cluster within the posterior amygdala and anterior hippocampus, as well as in the posterior insula across all three domains. Moreover, subject-specific amygdala responses to fearful music and vocalizations were correlated, consistent with the proposal that the brain circuitry involved in the processing of musical emotions might be shared with the one that have evolved for vocalizations. Overall, our results show that processing of fear expressed through music, engages some of the same brain areas known to be crucial for detecting and evaluating threat-related information.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 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".