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

Interoceptive interventions offer a promising avenue for improving mental health conditions, which commonly feature bodily or interoceptive symptoms. Perceptual accuracy for interoceptive signals, such as heartbeats, varies across individuals and presents a potential target for treatment. Adult participants (N = 28, 20F) completed eight sessions of a cardiac interoception training protocol, and their anxiety reduction was compared to that in a passive control group (N = 26, 22F). Bayesian computational models were compared to identify mechanisms of perception and learning that best explained participants' responses during the heartbeat discrimination task. Parameter estimates from the best-fitting model were used as computational phenotypes to explain anxiety reduction due to interoceptive training. Interoceptive training improved perceptual accuracy in two tasks of heartbeat perception and reduced self-reported trait anxiety. Computational modelling indicated that accuracy improvement in the heartbeat discrimination task was explained by increases in the internal reliability estimate for interoceptive signals - their precision weighting - while a lower-level parameter representing stable sensory noise moderated this precision weighting improvement by influencing the speed of learning. Reductions in both state and trait anxiety scores in the training group were uniquely explained by computational parameter estimates, and not by conventional accuracy measures. These findings indicate that cardiac interoceptive accuracy is modifiable and can be targeted to alleviate anxiety symptoms, and that interoceptive interventions may be best guided by a computational phenotyping approach.