Crossmodal Adaptation in Right Posterior Superior Temporal Sulcus during Face–Voice Emotional Integration
Bibliographic record
Abstract
The integration of emotional information from the face and voice of other persons is known to be mediated by a number of "multisensory" cerebral regions, such as the right posterior superior temporal sulcus (pSTS). However, whether multimodal integration in these regions is attributable to interleaved populations of unisensory neurons responding to face or voice or rather by multimodal neurons receiving input from the two modalities is not fully clear. Here, we examine this question using functional magnetic resonance adaptation and dynamic audiovisual stimuli in which emotional information was manipulated parametrically and independently in the face and voice via morphing between angry and happy expressions. Healthy human adult subjects were scanned while performing a happy/angry emotion categorization task on a series of such stimuli included in a fast event-related, continuous carryover design. Subjects integrated both face and voice information when categorizing emotion-although there was a greater weighting of face information-and showed behavioral adaptation effects both within and across modality. Adaptation also occurred at the neural level: in addition to modality-specific adaptation in visual and auditory cortices, we observed for the first time a crossmodal adaptation effect. Specifically, fMRI signal in the right pSTS was reduced in response to a stimulus in which facial emotion was similar to the vocal emotion of the preceding stimulus. These results suggest that the integration of emotional information from face and voice in the pSTS involves a detectable proportion of bimodal neurons that combine inputs from visual and auditory cortices.
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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) | 0.002 | 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".