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Record W4406277768 · doi:10.1016/j.heares.2025.109184

Toward cognitive models of misophonia

2025· review· en· W4406277768 on OpenAlexafffund
Marie‐Anick Savard, Emily B. J. Coffey

Bibliographic record

VenueHearing Research · 2025
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsConcordia UniversityInternational Laboratory for Brain, Music and Sound Research
FundersCanadian Institutes of Health ResearchFonds de recherche du QuébecREAM FoundationNatural Sciences and Engineering Research Council of CanadaMisophonia Research Fund
KeywordsPsychologyCognitionAudiologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Misophonia is a disorder in which specific common sounds such as another person breathing or chewing, or the ticking of a clock, cause an atypical negative emotional response. Affected individuals may experience anger, irritability, annoyance, disgust, and anxiety, as well as physiological autonomic responses, and may find everyday environments and contexts to be unbearable in which their 'misophonic stimuli' (often called 'trigger sounds') are present. Misophonia is gradually being recognized as a genuine problem that causes significant distress and has negative consequences for individuals and their families. It has only recently come under scientific scrutiny, as researchers and clinicians are establishing its prevalence, distinguishing it from other disorders of sensory sensitivity such as hyperacusis, establishing its neurobiological bases, and evaluating the effectiveness of potential treatments. While ideas abound as to the mechanisms involved in misophonia, few have coalesced into models. The aim of the present work is to summarize and extend recent thinking on the mechanistic basis of misophonia, with a focus on moving towards neurologically-informed cognitive models that can (a) account for extant findings, and (b) generate testable predictions. We hope this work will facilitate future refinements in our understanding of misophonia, and ultimately inform treatments.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.556
GPT teacher head0.526
Teacher spread0.030 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2025
Admission routes2
Has abstractyes

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