Measuring Misophonia: Assessing the psychometric properties of the MisoQuest and its ability to predict cognitive impacts of triggering sounds
Bibliographic record
Abstract
Misophonia is a disorder involving an aversion to specific ordinary sounds, such as chewing and breathing. These “trigger” sounds are easily ignored by typically developed listeners, but elicit negative emotional reactions, physiological stress, and cognitive impairment in people with misophonia. While the severity of this reaction differs across individuals, it is often accompanied by psychological distress. Nevertheless, misophonia is not yet classified as a psychological disorder in diagnostic manuals, largely because it is unclear how it should be defined and assessed. Accordingly, the current study aimed to assess the utility of the English language version of the MisoQuest - a recently developed measure of misophonia severity - in a sample of 139 participants, including 44 people with misophonia and 95 controls (96 female, 34 male, 9 transgender/non-binary/non-conforming/agender; 90 White/Caucasian, 10 Black/African Descent, 10 East Asian, 10 South Asian, 3 Middle Eastern/Arab, 3 Latinx/Hispanic, and 13 people from mixed ethnic backgrounds). We first demonstrate that, similar to the original Polish assessment, the English MisoQuest has excellent internal consistency and strong test-retest reliability. Additionally, we provide the first evidence that the MisoQuest specifically taps misophonia symptom severity rather than generalized anxiety or broader sensory sensitivities. Finally, we establish evidence of criterion validity, demonstrating that higher MisoQuest scores predict poorer performance on cognitive tasks in the presence of trigger sounds. Overall, this study indicates that the MisoQuest is a reliable and useful measure for identifying misophonia in English-speaking individuals and that scores on this measure are related to clinically relevant outcomes.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".