The scientific challenge of consciousness is not merely to identify neural correlates, but to explain why and when certain physical–informational processes are accompanied by subjective experience. The target explanandum in this manuscript is phenomenal consciousness, i.e., what-it-is-like-ness (Chalmers, 1995). Contemporary neuroscience emphasizes large-scale coordination and ignition-like dynamics in conscious access (Dehaene and Changeux, 2011), and state-dependent breakdowns of global coordination during anesthesia and sleep (Mashour et al., 2020).

Predictive processing and the Free Energy Principle (FEP) model organisms as systems that maintain themselves by minimizing variational free energy through inference and action (Friston, 2010; Clark, 2013). Integrated Information Theory (IIT) proposes that consciousness relates to intrinsic causal–informational structure quantified by Φ (Tononi, 2004). Despite their power, these frameworks leave open a bridging question: what additional constraint differentiates merely functional inference from phenomenal presence?

This manuscript proposes a minimal candidate constraint:

Consciousness, in this view, is associated with a regime of predictive dynamics—neither informational stasis nor unconstrained flux—where uncertainty exchange becomes locally balanced while inference remains active.

A key objection is that a system could satisfy “no net entropy flux” trivially if Js = 0, i.e., if there is no information flow at all. This objection is valid—and it is why closure must be dynamic.

Accordingly, this manuscript proposes three jointly necessary constraints for candidate phenomenal consciousness:

The first constraint rules out inert “empty” systems; the second rules out purely feedforward prediction; the third rules out unconstrained uncertainty exchange. In FEP terms, this corresponds to balancing accuracy and complexity rather than collapsing into trivial underfitting or pathological overfitting (Friston, 2010).

A common critique is that “equilibrium” seems incompatible with lived dynamics: learning, surprise, conflict, temporal flow. The key clarification is that resonant closure is metastable and local, not global and static.

The FEP already treats organisms as nonequilibrium steady states maintained by continuous exchange (Friston, 2010; Mediano et al., 2022). Equation 1 should therefore be read as a local balance constraint over time windows relevant to conscious integration (hundreds of milliseconds to seconds), not as global stationarity.

Surprise and learning can be interpreted as controlled, transient perturbations of closure that trigger model revision; closure is then re-established at a new model configuration (Barrett and Seth, 2011; Bastos et al., 2012; Bohm, 1980; Carhart-Harris and Friston, 2019; Fries, 2015; Friston et al., 2006, 2020; Hohwy, 2013; Penrose and Hameroff, 2014; Seth and Friston, 2016; Tononi and Koch, 2016). This is compatible with global workspace dynamics where conscious access can co-occur with high local prediction error while global organization remains viable (Dehaene and Changeux, 2011; Mashour et al., 2020).

If resonant closure approximates a sufficient condition, artificial systems should require: (i) recursive predictive inference, (ii) self-modeling of inferential state, and (iii) bounded uncertainty exchange.

Global Workspace architectures already emphasize broadcast/coordination as a functional signature of conscious access (Baars and Franklin, 2009). The present account adds a boundary-condition criterion: the system must stabilize a self-referential inferential loop with locally minimized uncertainty exchange. More broadly, work on reconciling deep learning with structured representations suggests concrete routes to implementing internal self-modeling components needed for recursion and closure (Garnelo and Shanahan, 2019).

The idea that “closure yields stable identity” invites analogy with symmetry breaking and order parameters in physics. The Higgs mechanism is a canonical example where symmetry breaking yields persistent properties (mass) (Higgs, 1964; Englert and Brout, 1964). Any use of this analogy here is structural rather than literal: the claim is not that a physical Higgs field exists in the brain, but that stable macroscopic properties can arise from self-consistent interactions.

Related “emergent from informational/thermodynamic constraints” perspectives appear in discussions of emergent gravity (Verlinde, 2011) and information-centric physics proposals (Wheeler, 1990). A relational stance in quantum foundations likewise treats physical description as fundamentally correlation-based, which motivates informational relationality without implying quantum mysticism (Rovelli, 1996). Thermodynamic derivations connecting spacetime dynamics and entropy illustrate the broader plausibility of deep links between physical law and informational constraints (Jacobson, 1995). Consciousness-as-a-state-of-matter proposals provide additional context for treating consciousness as a phase-like regime rather than a separate substance (Tegmark, 2015).

Where topology is referenced, it is intended heuristically to express robustness of organization rather than a computed invariant; for rigorous treatments of topological robustness in physics, see (Haldane, 2017).

Consciousness is proposed here as a dynamically self-stabilized informational regime: resonant closure, where recursive predictive inference becomes phase-coherent and net uncertainty exchange is locally minimized while internal informational dynamics remain high. The account (i) clarifies the explanandum (phenomenal consciousness), (ii) avoids trivial “empty system” closure, (iii) reconciles closure with dynamical experience via metastability, and (iv) yields predictions testable with neurophysiological and perturbational methods (Casali et al., 2013; Schartner et al., 2015).

If supported, resonant closure reframes consciousness as a lawful phase of information-processing dynamics—neither an epiphenomenal add-on nor a mere synonym for function, but a regime characterized by recursive self-modeling under dynamically maintained entropic boundary conditions.