Functional Consciousness Collapse Theory (FCCT)

Functional Consciousness Collapse Theory (FCCT) models consciousness not as a meta- physical entity but as a computable selection dynamic emerging from situational information. At each moment, conscious content is determined by the interaction of three components: the sensory state St ∈ RnS, the memory structure Mt ∈ RnM, and a priority vector Wt ∈ ∆k−1 encoding value weights. Together, these define a cognitive state space X in which the system’s internal dynamics unfold. In FCCT, the conscious state Ct arises through a stochastic collapse process: candidate states generated by a context-dependent kernel µt are evaluated through a scoring function f, producing a policy distribution of the form πt(x) ∝ exp (βtf (x, St, Mt, Wt)). The collapse Ct ∼ πt selects one candidate as the momentary conscious content. The theory quantifies consciousness level via the divergence metric Lt = DKL(πt ∥ Pt), which captures how strongly the policy departs from a background distribution and correlates with attention, focus, and decisional clarity. FCCT formalizes subjective experience (qualia) by introducing an equivalence relation on X × S: two states (x1, S1) and (x2, S2) belong to the same qualia class if they yield identical functional outputs for the subject. This induces a quotient space Q = (X × S)/ ∼. The experimentally used reverse-engineering map Φ : X × S → Rm provides a coordinate system for this abstract space; Φ is learnable and allows empirical identification of qualia equivalence classes. Overall, FCCT provides a mathematically precise framework that resolves the homunculus problem, grounds subjective quality in functional equivalence, and reframes free will in terms of memory and value-update dynamics. The theory predicts that two systems with identical (S, M, W ) configurations must yield identical conscious states a claim testable via neuroscientific measurements, clinical observations, and artificial agents. In summary, FCCT recasts consciousness as a scientifically examinable and computation- ally modelable phenomenon, defined through state spaces, stochastic selection dynamics, and equivalence structures.