Cardiac belief updating from volatile physiological afferents

Cardiac interoception is commonly assessed by comparing subjective estimates of heart rate with physiological measurements. But beliefs can easily obscure these measures, such that they cannot readily distinguish sensitivity to afferent signals from the influence of prior expectations; this ultimately challenges the notion that behaviours under these tasks could reflect interoceptive processes at all. Here, we develop a computational framework that uses naturally occurring heart rate variability to quantify how strongly perceptual beliefs are updated by physiological evidence. We formalise interoception as weighted Bayesian updating under the joint influence of expectations and afferents, and derive a new measure, cardiac interoceptive sensitivity, that quantifies the extent to which beliefs move with incoming signals. Applying this to the largest Heart Rate Discrimination dataset to date (n=549), a task providing robust estimates of cardiac beliefs, we find that sensitivity is weak in the healthy population, but shows large interindividual differences, with 43% of the participants exhibiting behaviours at least minimally compatible with interoceptive processing. These findings challenge the interpretation of conventional measures of cardiac interoception. They also introduce a new framework to relate bodily signals that cannot be controlled experimentally to external signals that can, laying a computational foundation for embodied psychophysics.