Computational Models of Predictive Processing in the Brain

Predictive processing is an influential framework in neuroscience that posits the brain as a prediction machine—constantly generating and updating internal models to anticipate sensory inputs and minimize prediction errors. This theory offers a unifying account of perception, action, and cognition, suggesting that the brain operates through hierarchical inference and continuous feedback loops. Computational models have become essential tools in exploring predictive processing, enabling researchers to formalize hypotheses, simulate neural mechanisms, and test predictions against empirical data. Recent advances in probabilistic modeling, Bayesian inference, and machine learning have significantly expanded our ability to model these complex processes. These models are increasingly applied across domains, from sensory perception to motor control and higher cognition. As such, computational modeling of predictive processing sits at the intersection of neuroscience, cognitive science, and artificial intelligence, providing a powerful lens for understanding brain function and its dysfunctions.

Predictive processing is a powerful framework, but many of its core mechanisms remain underdefined and lack clear computational models. Challenges include modeling hierarchical predictions, quantifying prediction errors, and linking them to neural and behavioral data. The variety of modeling approaches—from Bayesian inference to deep learning—also raises questions about their biological relevance. Recent advances in computational neuroscience, probabilistic modeling, and neural recording and manipulation now offer tools to tackle these issues. This Research Topic seeks contributions that develop, compare, or validate computational models of predictive processing. We encourage studies that integrate theory, simulation, and empirical data to clarify how the brain anticipates and responds to its environment.

This Research Topic focuses on advancing computational models of predictive processing in the brain. We invite work on hierarchical predictive coding, neural representations of prediction errors, learning mechanisms, and the role of predictive processing in perception, decision-making, and mental health. Studies combining computational models with neuroimaging, electrophysiology, or behavioral data are encouraged. We welcome original research, theoretical papers, methodological developments, and reviews. Interdisciplinary submissions bridging neuroscience, cognitive science, and machine learning are particularly valued. The goal is to clarify how predictive processing supports adaptive brain function.