Rethinking Predictive Processing

Predictive coding proposes that the brain constructs internal models of the world to continuously predict sensory input and uses resulting errors to refine these models. Over the past several decades, neurophysiological studies have reported activity patterns consistent with this view. However, varied definitions and inconsistent empirical evidence have raised questions about its validity and explanatory scope. In this review, we provide a historical overview of the predictive processing framework and evaluate its empirical support, particularly focusing on sensory prediction error signals. We argue that clarifying what information these signals represent is crucial, as they may appear similar in their responses yet reflect fundamentally different underlying computations. We then revisit predictive coding, highlighting alternative accounts of how sensory prediction error signals encode information. Finally, we outline key directions for future work, aiming to provide a constructive roadmap for the next phase of predictive processing research and to advance our understanding of the neuronal algorithms underlying perception and cognition.