Summary

This collection argues that predictive processing, a computational theory of brain function, offers a powerful new lens for understanding foundational philosophical problems. The central thesis is that a scientifically informed predictive processing framework can reframe questions about consciousness, free will, the self, and the nature of reality, moving them from armchair speculation towards empirically grounded inquiry. The essays explore how the brain actively generates models of the world and itself, and how failures or specific configurations of this generative process might explain subjective experience, agency, and the feeling of being a unified self.

Readers gain a sophisticated understanding of how a dominant neuroscience paradigm can intersect with and potentially resolve long-standing philosophical debates. Key takeaways include appreciating the predictive brain as a hypothesis generator constantly testing its internal models against sensory input, and how this process is fundamental to perception, decision-making, and self-awareness. The book highlights the potential for computational neuroscience to provide concrete, testable hypotheses for phenomena previously considered intractable.

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Key concepts

Generative Model — A predictive processing model where the brain generates hypotheses about the causes of sensory input.
Prediction Error Minimization — The core principle that the brain updates its internal models to reduce discrepancies between predicted and actual sensory signals.
Global Workspace Theory — A theory of consciousness that, when integrated with predictive processing, suggests conscious content arises from a shared informational workspace.
Bayesian Brain Hypothesis — The idea that the brain performs computations akin to Bayesian inference, weighting prior beliefs with new evidence.
Extended Predictive Processing — The concept that predictive processing might extend beyond the brain to encompass the body and environment.