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Predictive processing as a book category collects works that treat the mind, brain, or intelligent systems as prediction engines: systems that continuously generate hierarchical, probabilistic models of the world and update them by minimizing prediction error (often framed in terms of Bayesian inference, active inference, or the free-energy principle). Books in this category range from technical textbooks and computational-model handbooks to empirical neuroscience studies, philosophical and conceptual analyses, clinical and psychiatric applications, and popular-science accounts that explain perception as “controlled hallucination.” They emphasize themes such as perception, attention, learning, action, consciousness, and the implications for artificial intelligence and robotics, and are typically interdisciplinary—appealing to cognitive scientists, neuroscientists, philosophers, AI researchers, clinicians, and informed general readers. Whether exploring mathematical formalisms, experimental evidence, therapeutic approaches, or broader cultural and epistemological consequences, predictive processing books aim to reframe how we understand cognition as prediction-driven and model-based.

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