Navigating the Predictive Self: Metastyling as Architecture of Stabilizable Identity Configurations

The predictive processing account of selfhood — developed by Seth (*Being You*, 2021), Friston (*Nature Reviews Neuroscience*, 2010), Damasio (*Self Comes to Mind*, 2010), and Varela, Thompson and Rosch (*The Embodied Mind*, 1991) — establishes that identity is a dynamic biological construction: a generative model continuously updated through interoceptive inference and active engagement with an uncertain environment. This account provides a compelling mechanistic description of how the self is produced, but it does not supply an architecture of the configurations the predictive self can occupy: a formalization of stable identity states, the topology of movement between them, and the structural conditions under which intentional navigation of that topology becomes possible. This paper proposes that the Metastyling framework (Pau, 2025) provides precisely this architecture. We argue that stable identity configurations — designated Faces — function as attractor basins in a high-dimensional predictive landscape, characterized by DMES state vectors (Direction, Meaning, Expression, State) and indexed by attractor depth parameter β, which corresponds formally to the precision-weighting of the configuration's prior beliefs in Friston's active inference framework. Transition Cost between configurations is formalized as a function of attractor depth, configurational distance, meta-cognitive capacity, and transition time — an operationalization of the free energy expenditure required for identity model revision. Levels of Awareness (LoA) are introduced as the trainable meta-cognitive capacity that modulates the organism's relationship to its own prediction error, determining the range of navigational agency available at any moment. Together, these constructs constitute a topological account of identity that dissolves the opposition between essentialist and constructionist models, explains the structural resistance to identity change, and generates testable predictions at physiological, behavioral, and developmental levels.