Directionality as a Latent State A Minimal State-Estimation Formalization of Human Adaptation

Boundary note (added 2026-07-23): the Ω-centered empirical layer was self-retracted on 2026-05-31. Ω-centered validation is not currently supported as the active empirical basis; this record remains a historical, fixed, Ω-centered record and must not be cited as current validation. See the VOT Empirical Layer Boundary Note — https://doi.org/10.5281/zenodo.20474232. A later four-dataset head-to-head found that order gated by critical slowing does not beat free-energy signals (10.5281/zenodo.21422215, negative result). Human well-being, adaptation, and breakdown are commonly described using static psychological or social indicators. This paper proposes an alternative structural framing: human adaptation is better characterized as a latent state estimation problem. A latent directionality potential, Ω (Omega), is introduced as an unobservable state governing long-term adaptive dynamics. Rather than focusing on Ω itself, the framework centers on its temporal derivative, V = dΩ/dt, as the primary inferential target. The sign and persistence of dΩ/dt are argued to provide more structural information about adaptation, recovery, and collapse than static state variables such as satisfaction, affect, or reward. The paper formalizes this idea using a minimal state-space representation, clarifying conceptual alignment with classical estimation frameworks (e.g., Kalman or particle filtering) without proposing specific estimators, datasets, or empirical validation. Existing time-series indicators are positioned as observable state variables, while dΩ/dt functions as a leading indicator describing directional momentum rather than current condition. A minimal extension to collective dynamics is outlined, suggesting that large-scale instability arises from variance-driven directional incoherence rather than aggregate state decline. This work makes no normative, predictive, or intervention claims. It is intended solely as a conceptual and structural foundation, defining a new estimand and problem framing for future theoretical, empirical, and computational research.