Audio and visual speech emotion activates the left pre-supplementary motor area
Bibliographic record
Abstract
Sensorimotor brain areas have been implicated in the recognition of emotion expressed on the face and through non-verbal vocalizations. However, no previous study has assessed whether sensorimotor cortices are recruited during the perception of emotion in speech, a signal that includes both audio (speech sounds) and visual (facial speech movements) components. To address this gap in the literature, we recruited 24 participants to listen to speech clips expressed in a way that was either a happy, sad, or neutral. These stimuli were also presented in one of three modalities: audio-only (hearing the voice but not seeing the face), video-only (seeing the face but not hearing the voice), or audiovisual. Brain activity was recorded using electroencephalography, subjected to independent component analysis, and source-localized. We found that the left pre-supplementary motor area was more active in response to happy and sad stimuli than neutral stimuli, as indexed by greater mu event-related desynchronization. This effect did not differ by the sensory modality of the stimuli. Activity levels in other sensorimotor brain areas did not differ by emotion, although they were greatest in response to visual-only and audiovisual stimuli. One possible explanation for the pre-SMA result is that this brain area may actively support speech emotion recognition by using our extensive experience expressing emotion to generate sensory predictions that in turn guide our perception.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".