From categories to dimensions: spatio-temporal dynamics of the cerebral representations of emotion in voice
Bibliographic record
Abstract
Abstract Whether the human brain represents emotional stimuli as discrete categories or continuous dimensions is still widely debated. Here we directly contrasted the power of categorical and dimensional models at explaining behavior and cerebral activity in the context of perceived emotion in the voice. We combined functional magnetic resonance imaging (fMRI) and magneto-encephalography (MEG) to measure with high spatiotemporal precision the dynamics of cerebral activity in participants who listened to voice stimuli expressing a range of emotions. The participants also provided a detailed perceptual assessment of the stimuli. By using representational similarity analysis (RSA), we show that the participants’ perceptual representation of the stimuli was initially dominated by discrete categories and an early (<200ms) cerebral response. These responses showed significant associations between brain activity and the categorical model in the auditory cortex starting as early as 77ms. Furthermore, we observed strong associations between the arousal and valence dimensions and activity in several cortical and subcortical areas at later latencies (>500ms). Our results thus show that both categorical and dimensional models account for patterns of cerebral responses to emotions in voices but with a different timeline and detail as to how these patterns evolve from discrete categories to progressively refined continuous dimensions. One Sentence Summary: Emotions expressed in the voice are instantly categorized in cortical processing and their distinct qualities are refined dimensionally only later on.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".