A machine learning approach towards the differentiation between interoceptive and exteroceptive attention.

ABSTRACT: Interoception, the representation of the body's internal state, plays a central role in emotion, motivation and wellbeing. Interoceptive sensibility, the ability to engage in sustained interoceptive awareness, is particularly relevant for mental health but is exclusively measured via self-report, without methods for objective measurement. We used machine learning to classify interoceptive sensibility by contrasting using data from a randomized control trial of interoceptive training, with functional magnetic resonance imaging assessment before and after an 8-week intervention (N = 44 scans). The neuroimaging paradigm manipulated attention targets (breath vs. visual stimuli) and reporting demands (active reporting vs. passive monitoring). Machine learning achieved high accuracy in distingui

SUBMITTER: Zuo ZX 

PROVIDER: S-EPMC10727490 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature