The Time Course of the Pupillary Response to Auditory Emotions in Pseudospeech, Music, and Vocalizations
Bibliographic record
Abstract
Emotions can be communicated through visual and dynamic characteristics such as smiles and gestures, but also through auditory channels such as laughter, music, and human speech. Pupil dilation has become a notable marker for visual emotion processing; however the pupil's sensitivity to emotional sounds, specifically speech, remains largely underexplored. This study investigated the processing of emotional pseudospeech, which are speech-like sentences devoid of semantic content. We measured participants' pupil dilations while they listened to pseudospeech, music, and human vocalizations, and subsequently performed an emotion recognition task. Our results showed that emotional pseudospeech can trigger increases of pupil dilation compared to neutral pseudospeech, supporting the use of pupillometry as a tool for indexing prosodic emotion processing in the absence of semantics. However, pupil responses to pseudospeech were smaller and slower than the responses evoked by human vocalizations. The pupillary response was not sensitive enough to distinguish between emotion categories in pseudospeech, but pupil dilations to music and vocalizations reflected some emotion-specific pupillary curves. The valence of the stimulus had a stronger overall influence on pupil size than arousal. These results highlight the potential for pupillometry in studying auditory emotion processing and provide a foundation for contextualizing pseudospeech alongside other affective auditory stimuli.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".