A phenomenological cartography of misophonia and other forms of sound intolerance

Nora Andermane; Mathilde Bauer; Ediz Sohoglu; Julia Simner; Jamie Ward

doi:10.1016/j.isci.2023.106299

A phenomenological cartography of misophonia and other forms of sound intolerance

iScience. 2023 Feb 28;26(4):106299. doi: 10.1016/j.isci.2023.106299. eCollection 2023 Apr 21.

Authors

Nora Andermane¹, Mathilde Bauer¹, Ediz Sohoglu¹, Julia Simner¹, Jamie Ward¹

Affiliation

¹ School of Psychology, University of Sussex, Brighton, UK.

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. 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.

Keywords: Behavioral neuroscience; Cognitive neuroscience; Health sciences.