Qualia as Multilayer Adaptive Fixed Points: A GIP-TTA Framework for Phenomenal Consciousness
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes that qualia are multilayer adaptive fixed points emerging within hierarchical generative architectures that continuously adapt to their environment, and argues that mapping the Genesis-Integration Principle onto multiscale Test-Time Adaptation dynamics provides a framework for understanding qualia as adaptive fixed points in the coupled space of internal representations, prediction-error flows, and action policies, offering a middle path between purely structural and purely phenomenological accounts of consciousness. |
Abstract
What are qualia in a form that can be jointly addressed by phenomenology, neuroscience, and artificial intelligence? This paper addresses this question by focusing on the dynamical organization shared by adaptive biological and artificial systems. I propose that qualia are multilayer adaptive fixed points emerging within hierarchical generative architectures that continuously adapt to their environment. Building on the Genesis-Integration Principle (GIP) and recent advances in Test-Time Adaptation (TTA), I argue that phenomenal qualities correspond to dynamically stabilized patterns that persist across multiple timescales of adaptation. Within the GIP framework, adaptive systems evolve through recursive cycles of genesis, integration, and optimization, yielding stable yet flexible internal configurations. TTA provides a concrete computational realization of such dynamics in artificial agents adapting online to distributional shifts. By mapping GIP processes onto multiscale TTA dynamics, the paper shows how qualia can be understood as adaptive fixed points in the coupled space of internal representations, prediction-error flows, and action policies. This framework offers a middle path between purely structural and purely phenomenological accounts of consciousness, and provides a principled basis for comparing biological and artificial systems without reducing one to the other.