Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network

According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refning these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to defne the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We frst use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we fnd that: (i) predictive processing seems to occur more in certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing