Adaptation to Vocal Expressions Reveals Multistep Perception of Auditory Emotion
Bibliographic record
Abstract
The human voice carries speech as well as important nonlinguistic signals that influence our social interactions. Among these cues that impact our behavior and communication with other people is the perceived emotional state of the speaker. A theoretical framework for the neural processing stages of emotional prosody has suggested that auditory emotion is perceived in multiple steps (Schirmer and Kotz, 2006) involving low-level auditory analysis and integration of the acoustic information followed by higher-level cognition. Empirical evidence for this multistep processing chain, however, is still sparse. We examined this question using functional magnetic resonance imaging and a continuous carry-over design (Aguirre, 2007) to measure brain activity while volunteers listened to non-speech-affective vocalizations morphed on a continuum between anger and fear. Analyses dissociated neuronal adaptation effects induced by similarity in perceived emotional content between consecutive stimuli from those induced by their acoustic similarity. We found that bilateral voice-sensitive auditory regions as well as right amygdala coded the physical difference between consecutive stimuli. In contrast, activity in bilateral anterior insulae, medial superior frontal cortex, precuneus, and subcortical regions such as bilateral hippocampi depended predominantly on the perceptual difference between morphs. Our results suggest that the processing of vocal affect recognition is a multistep process involving largely distinct neural networks. Amygdala and auditory areas predominantly code emotion-related acoustic information while more anterior insular and prefrontal regions respond to the abstract, cognitive representation of vocal affect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".