A phenomenological cartography of misophonia and other forms of sound intolerance.

ABSTRACT: People with misophonia have strong aversive reactions to specific "trigger" sounds. 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

SUBMITTER: Andermane N 

PROVIDER: S-EPMC10156615 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature