Seeing a Face in a Crowd of Emotional Voices: Changes in Perception and Cortisol in Response to Emotional Information across the Senses
Bibliographic record
Abstract
One source of information we glean from everyday experience, which guides social interaction, is assessing the emotional state of others. Emotional state can be expressed through several modalities: body posture or movements, body odor, touch, facial expression, or the intonation in a voice. Much research has examined emotional processing within one sensory modality or the transfer of emotional processing from one modality to another. Yet, less is known regarding interactions across different modalities when perceiving emotions, despite our common experience of seeing emotion in a face while hearing the corresponding emotion in a voice. Our study examined if visual and auditory emotions of matched valence (congruent) conferred stronger perceptual and physiological effects compared to visual and auditory emotions of unmatched valence (incongruent). We quantified how exposure to emotional faces and/or voices altered perception using psychophysics and how it altered a physiological proxy for stress or arousal using salivary cortisol. While we found no significant advantage of congruent over incongruent emotions, we found that changes in cortisol were associated with perceptual changes. Following exposure to negative emotional content, larger decreases in cortisol, indicative of less stress, correlated with more positive perceptual after-effects, indicative of stronger biases to see neutral faces as happier.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".