Timing along the cardiac cycle modulates neural signals of reward-based learning

Natural fluctuations in cardiac activity influence brain activity associated with sensory stimuli and affect perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the impact of fluctuations in heart activity on other internal representations. Here we investigate cardiac influences on learning-related internal representations - absolute and signed prediction errors. By combining machine learning techniques with electroencephalography (EEG) and both simple, direct indices of task performance and computational model-derived indices of learning, we demonstrate that just as people are more sensitive to low magnitude, near threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. Importantly, however, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger difference in their prediction errors representations between cardiac cycles exhibited higher learning rates and greater task accuracy.