A phenomenological cartography of misophonia and other forms of sound intolerance

People with misophonia have strong aversive reactions to specific triggersounds. Here we challenge this key idea of specificity. Machine learning was used to identify a misophonic profile from a multivariate sound-response pattern. Misophonia could be classified from most sounds (traditional triggers and non -triggers) and, moreover, cross-classification showed that the profile was largely transferable across sounds (rather than idiosyncratic for each sound). By splitting our participants in other ways, we were able to show-using the same approach-a differential diagnostic profile factoring in potential co-morbidities (autism, hyperacusis, ASMR). The broad autism phenotype was classified via aversions to repetitive sounds rather than the eating sounds most easily classified in misophonia. Within misophonia, the presence of hyperacusis and sound-induced pain had widespread effects across all sounds. Overall, we show that misophonia is characterized by a distinctive reaction to most sounds that ultimately becomes most noticeable for a sub-set of those sounds.

Automated summary

People with misophonia have strong aversive reactions to specific trigger sounds. Machine learning was used to identify a misophonic profile that is transferable across different sounds. Misophonia is characterized by a distinctive reaction to most sounds, with the presence of other co-morbidities like autism and hyperacusis affecting the classification.