4.3 Article

What sound sources trigger misophonia? Not just chewing and breathing

Journal

JOURNAL OF CLINICAL PSYCHOLOGY
Volume 77, Issue 11, Pages 2609-2625

Publisher

WILEY
DOI: 10.1002/jclp.23196

Keywords

diagnosis; machine learning; misophonia; sound aversion; sound sensitivity; source categories

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Misophonia is more than just an aversion to oral/nasal sounds, as individuals with this condition rated sounds from all three source categories as highly aversive. Machine-learning algorithms showed potential in predicting the presence and severity of misophonia.
Objectives Misophonia is a highly prevalent yet understudied condition characterized by aversion toward particular environmental sounds. Oral/nasal sounds (e.g., chewing, breathing) have been the focus of research, but variable experiences warrant an objective investigation. Experiment 1 asked whether human-produced oral/nasal sounds were more aversive than human-produced nonoral/nasal sounds and non-human/nature sounds. Experiment 2 additionally asked whether machine-learning algorithms could predict the presence and severity of misophonia. Method Sounds were presented to individuals with misophonia (Exp.1: N = 48, Exp.2: N = 45) and members of the general population (Exp.1: N = 39, Exp.2: N = 61). Aversiveness ratings to each sound were self-reported. Results Sounds from all three source categories-not just oral/nasal sounds-were rated as significantly more aversive to individuals with misophonia than controls. Further, modeling all sources classified misophonia with 89% accuracy and significantly predicted misophonia severity (r = 0.75). Conclusions Misophonia should be conceptualized as more than an aversion to oral/nasal sounds, which has implications for future diagnostics and experimental consistency moving forward.

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