Active Inference - TOWARDS LIFE-KNOWLEDGE

Despite sustained advances in neuroscience, psychiatry, philosophy of mind, and artificial intelligence, subjective experience — qualia — remains resistant to explanation. Traditional approaches frame consciousness as something produced by physical processes, leaving an apparent explanatory gap between third-person descriptions and first-person experience.

This book proposes a reframing. Rather than treating consciousness as an emergent output, it argues that qualia are the interior face of viability wherever a system must preserve its own coherence under uncertainty through lossy interfaces. From this perspective, experience is not mysterious but inevitable: it arises when regulation cannot be further reduced without loss of function.

Integrating affective neuroscience, predictive processing, psychiatry, philosophy of mind, and ancient interior sciences such as Daoism, Traditional Chinese Medicine, and Ayurveda, the book develops a unified interface-based framework in which emotional sentience precedes cognition, affect grounds consciousness, and meaning emerges through layered projections. Competing theories — ranging from affective and constructionist models of emotion to active inference and the hard problem of consciousness — are re-situated at distinct interface depths rather than forced into premature synthesis.

The result is a rigorously naturalistic account that preserves the irreducibility of experience without invoking metaphysical dualism or reductionism. By locating qualia at the intersection of regulation, uncertainty, and intrinsic value, the framework offers new clarity for neuroscience, psychiatry, philosophy, and the ethics of artificial systems.

Abstract

The scientific process plays out in a multi-scale system comprising subsystems, each with their own properties and dynamics. For the practice of science to generate useful world models — and lead to the development of enabling technologies — practicing scientists, their theories, methods, dissemination, and infrastructure (e.g., funding and laboratories) must all fit together in an orchestrated manner. Scientific practice has broad societal implications that go beyond mere scientific progress: we base our decisions on theoretical (i.e., models and forecasts) and technological (e.g., vaccines and smartphones) scientific advances. This paper applies the free energy principle to provide a multi-scale description of science understood as evidence-seeking processes in a nested hierarchy of living (biological and behavioural) and epistemic (linguistic) structures. This allows us to naturalise the scientific process — as distributed self-evidencing — in terms of dynamics that can be read as inference or Bayesian belief updating; i.e., processes that maximize the evidence for a generative model of the sensed and measured world. The ensuing meta-theoretical approach dispels the notion of science as truth-pointing and foregrounds inference to the best explanation — as evinced by the beliefs of scientists and their encultured niche. Crucially, it furnishes a way of simulating the practice of science, which may have a foundational role in the next generation of augmented intelligence systems. Epistemologically, it also addresses some key questions; e.g., is science a special? And in what ways is scientific pursuit an existential imperative for all beings? These questions may be foundational in how we use and design intelligent systems.