Incorporating uncertainty within dynamic interoceptive learning
Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. Here we extended an interoceptive Breathing Learning Task (BLT) to incorporate continuous measures of prediction certainty, and tested its application using a Rescorla Wagner (RW) associative learning model. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances. The task was modified from a previous version and required continuous, rather than binary predictions, in order to include a more precise measure of prediction certainty. The RW model was used to fit a learning rate to each participants continuous and binarised predictions, and was additionally extended to test whether learning rates differed according to stimuli valence. The empirical task data demonstrated excellent replicability compared to previously collected data using binary predictions, and the continuous model fits closely captured participant behaviour at the group level. The model extension to estimate different learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) trials indicated that learning rates did not significantly differ according to stimuli nature. Furthermore, examining the relationship between estimates of prediction certainty and learning rates with interoceptive and mental health questionnaires demonstrated that fatigue severity was related to both prediction certainty and learning rate, and anxiety sensitivity was related to prediction certainty. The updated task and model show promise for future investigations into interoceptive learning and potential links to mental health.