A hierarchical Bayesian model reveals increased precision weighting for afferent cardiac signals, and reduced anxiety, as a function of interoceptive training

Abstract

Perceptual accuracy for interoceptive signals, such as heartbeats, varies in a trait-like manner across individuals and may influence the capacity for emotion regulation and vulnerability to affective symptoms, notably anxiety. Here, we demonstrate that an interoceptive training protocol improved perceptual accuracy in two tasks of heartbeat perception and reduced both state and trait anxiety in a subclinical sample, extending previous findings in autistic adults. Computational modelling indicated that accuracy improvement in the heartbeat discrimination task was associated with increases in the internal reliability estimate for interoceptive signals - their precision weighting - while a lower-level parameter representing noise in the interoceptive signal itself (which influences speed of learning) moderated this precision weighting improvement. Reductions in both state and trait anxiety in the training group were uniquely explained by computational parameter estimates, and not by conventional accuracy measures. These findings indicate that trait-like differences in interoceptive processing are modifiable and can be targeted to alleviate anxiety symptoms, and that interoceptive interventions may be best guided by a computational phenotyping approach.