Predictive Coding: From typical cognition to clinical conditions

Predictive Coding (PC) is a prominent theoretical framework that posits that a core function of the brain is a hierarchical inference process. It actively generates predictions about the causes of sensory input and continuously updates these based on "prediction errors" (the mismatch between expectation and reality). This parsimonious model offers a mechanistic account for a wide range of typical cognitive functions, from basic sensory inference and perceptual stability to motor control and decision-making.

The framework has now entered a phase where a sufficient body of research has emerged applying it to various clinical conditions. Dysregulation in core predictive processing mechanisms, such as the generation of predictions or the precision weighting of prediction errors, is increasingly posited as a transdiagnostic mechanism that may underlie symptoms across different disorders. This theoretical shift opens up new avenues for understanding pathophysiology and potential interventions.

This Research Topic aims to bridge the gap between foundational research on predictive coding in healthy cognition and its expanding application in clinical contexts. We seek a high-impact collection of articles that explore PC mechanisms across this spectrum, from computational modeling and basic sensory processing to complex symptoms and clinical dysregulation. By curating contemporary work from both computational/basic neuroscience and clinical domains, this collection will critically examine the predictive coding framework and help set the agenda for future research into its role in health and disease.

We welcome submissions of articles addressing themes including, but not limited to:

- Computational Models: Theoretical work on PC, hierarchical inference, and active inference.
- Typical Cognition: Neurophysiological (EEG, MEG, fMRI) and behavioral studies investigating PC mechanisms (e.g., mismatch negativity, repetition suppression) in healthy populations.
- Clinical Applications: Empirical studies applying the PC framework to various conditions, such as: Autism Spectrum Disorder (ASD), Psychosis spectrum disorders (e.g., Schizophrenia), Anxiety, depression, and obsessive-compulsive disorders, Neurodegenerative diseases and dementias (e.g., Alzheimer’s, Parkinson’s), Chronic pain and somatic symptom disorders, Functional neurological disorders
- Translational Mechanisms: Research linking specific computational parameters (e.g., precision weighting, learning rates) to transdiagnostic clinical symptom dimensions.
- Methodological Advances: New methods, experimental paradigms, or analytical techniques for probing PC mechanisms in human participants.
- Critical Perspectives: Submissions that critically evaluate the PC framework, test its core assumptions, or propose alternative models.