The vocal side of empathy: neural correlates of pain perception in spoken complaints
Bibliographic record
Abstract
In the extensive neuroimaging literature on empathy for pain, few studies have investigated how this phenomenon may relate to everyday social situations such as spoken interactions. The present study used functional Magnetic Resonance Imaging (fMRI) to assess how complaints, as vocal expressions of pain, are empathically processed by listeners and how these empathic responses may vary based on speakers' vocal expression and cultural identity. Twenty-four French participants listened to short utterances describing a painful event, which were either produced in a neutral-sounding or complaining voice by both in-group (French) and out-group (French Canadian) speakers. Results suggest that the perception of suffering from a complaining voice increased activity in the emotional voice areas, composed of voice-sensitive temporal regions interacting with prefrontal cortices and the amygdala. The Salience and Theory of Mind networks, associated with affective and cognitive aspects of empathy, also showed prosody-related activity and specifically correlated with behavioral evaluations of suffering by listeners. Complaints produced by in- vs out-group speakers elicited sensorimotor and default mode activity, respectively, suggesting accent-based changes in empathic perspective. These results, while reaffirming the role of key networks in tasks involving empathy, highlight the importance of vocal expression information and social categorization processes when perceiving another's suffering during social interactions.
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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 | 0.001 |
| 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".