Triggering Misophonia: The Importance of Spectral Information, Temporal Information, and Action Identification
Bibliographic record
Abstract
Misophonia is characterized by severe negative emotional responses to specific environmental sounds. In this experiment, we investigated the importance of spectral and temporal acoustic information, as well as the role of action identification (e.g., chewing) in triggering misophonia. Eighteen participants with severe misophonia completed the experiment. In total, three stimulus sets were used: the first set consisted of recorded sounds that were either common misophonic triggers or neutral sounds that are not expected to trigger misophonia; the other two sets were each generated by applying temporal and spectral modifications to the unmodified (recorded) stimulus set. Participants rated how triggered they were by each sound (i.e., aversiveness) and were asked to identify the action category of each sound. The unmodified trigger sounds were rated to be more aversive than neutral sounds (p < 0.0001). The main effects of modification type and identification on aversiveness of trigger sounds were significant (p = 0.0001 and p = 0.006, respectively), but their interaction was marginally significant (p = 0.053). Although the unmodified and temporally modified sounds were not significantly different from each other (p = 0.4), spectrally modified sounds were rated significantly less aversive than both the temporally modified (p = 0.003) and unmodified sounds (p = 0.001). Regarding identification, the sounds that were incorrectly identified were on average rated as less aversive than the correctly identified sounds (p = 0.006). Furthermore, the interaction shows that the identification effect was largest for the spectrally modified sounds. This shows that both identification and spectral information play an important role in triggering misophonia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".