Confidence of probabilistic predictions modulates the cortical response to pain

Abstract

Pain typically evolves over time and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the Vertex Potential: the more confident were participants about their predictions, the smaller was the Vertex Potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning. SIGNIFICANCEThe functional significance of EEG responses to pain has long been debated because of their dramatic variability. This study indicates that such variability can be partly related to the confidence of probabilistic predictions emerging from sequences of pain inputs. The confidence of pain predictions is negatively associated with the cortical EEG responses to pain. This indicates that the brain relies less on sensory inputs when confidence is higher and shows us that confidence-weighted statistical learning modulates the cortical response to pain.