Understanding as Dynamic Reconstruction of Reality: A Constraint-Based Operational Account within the Unified Dynamic Model of Mind

Abstract This paper develops a domain-relative operational account of understanding within the Unified Dynamic Model of Mind (UDMM). It distinguishes the possibilities licensed by a stated domain model, those available under current constraints and capacities, those represented by the evaluated system, and the distribution the system assigns to outcomes. It proposes a three-part assessment profile: constraint fidelity, interventional adequacy, and revisability under conflicting evidence, evaluated on preregistered held-out variations and compared with baselines matched for information and acquisition route. The formal analysis separates observed history from a transition-sufficient present state, shows why possibility-change scores depend on label granularity, and establishes that observational prediction does not uniquely determine intervention predictions. A finite-domain worked example and exact-arithmetic noisy-learning calculations illustrate how the scores can dissociate and how constraint judgements respond to sample size and observation noise. These constructed examples are not empirical evidence, and the framework does not establish necessary or sufficient conditions for human understanding. Its contribution is a testable measurement proposal whose value depends on independent evidence that its profile predicts transfer and revision beyond recall, surface matching, relational-transfer, predictive, and causal-learning baselines. A self-contained script reproducing the worked calculations is supplied as Supplementary File S1.