Advances in Predictive Coding: Evidence, Mechanisms and Challenges

Following the success of the original Predictive Coding Research Topic, this collection welcomes new contributions that further advance our understanding in this field. The study of predictive processing has become a cornerstone in perception science, aiming to explain how the brain anticipates and interprets sensory information. Initially formalized through predictive coding models, these frameworks propose that the brain constantly generates internal predictions and compares them to actual sensory input. Persistent questions exist around how accurately these models reflect the underlying neural mechanisms, especially as research extends into broader computational frameworks such as the Free Energy Principle and Active Inference, where perception, action, and learning are unified by the minimization of prediction error or variational free energy. Despite predictive processing’s theoretical appeal and integration into multiple domains, a critical need persists for robust empirical tests that conclusively distinguish predictive coding from alternative explanatory models.Recent years have seen significant methodological advances capable of probing the microcircuitry underlying predictive processing. Laminar fMRI and high-density electrophysiological studies have pinpointed prediction errors and top-down predictions as distinct, layer-specific neural signals, often mapped to different frequency bands such as gamma and alpha/beta. Cross-modal and hierarchical experiments have begun exploring how the brain combines predictions across sensory modalities, while computational models such as Predictive Coding Light (PCL) have demonstrated the biological plausibility and functional advantages of prediction-driven coding strategies. Despite this, debate remains about the interpretation of prediction error signals, the true role of oscillatory dynamics, and how individual or clinical differences shape predictive processing in perception. Many positive experimental findings remain reconcilable with competing theories, showing gaps in establishing clear neural signatures unique to predictive coding processes.This Research Topic aims to consolidate and examine new empirical evidence on predictive processing in perception. It seeks to promote dialogue between innovative experimental approaches and computational/theoretical frameworks, advancing our understanding of how predictions and errors are generated and coordinated within the brain. By clarifying which aspects of predictive processing are empirically validated, highlighting remaining ambiguities, and identifying priority areas for methodological and theoretical development, this collection aspires to set a new agenda for perception research grounded in predictive processing accounts.To gather further insights in empirical and theoretical analyses of predictive processing in perception, this collection welcomes articles addressing, but not limited to, the following themes:• Empirical tests of predictive coding mechanisms in perception, including feedback and feedforward signalling, and precision weighting• Application of advanced neuroimaging and neurophysiology (e.g., laminar fMRI, MEG/EEG source localization, intracranial recordings) to decode hierarchical and laminar dynamics• Exploration of developmental trajectories, individual differences, or clinical implications of predictive processing• Investigations into cross-modal, temporal, or embodied aspects of perceptual prediction• Computational and neural network models (including spiking networks) that refine or challenge predictive processing principles• Theoretical syntheses, systematic reviews, or meta-analyses clarifying empirical support and open questionsOriginal research articles, computational studies, methodological innovations, reviews, and meta-analyses that critically advance the field of predictive processing in perception are welcomed. Topic editors: Lars Muckli Source: https://www.frontiersin.org/research-topics/77099/advances-in-predictive-coding-evidence-mechanisms-and-challenges