In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presumes a correspondence between output metrics and internal system states that may not hold in adaptive systems. In this study, the vertical dimension of occlusion (VDO) is considered as a constraint applied to an adaptive neuromechanical system, enabling the exploration of system-level responses under controlled variations. A single-case design in a patient with Parkinson's disease allows an intra-individual analysis across repeated conditions.The analysis is structured across three complementary levels: (i) aggregated linear metrics describing observable performance, (ii) a dynamical systems framework describing temporal organization in state space, and (iii) a latent space representation obtained through unsupervised embedding. The results show that conditions with comparable observable performance may correspond to different organizations in both state space and latent space representations. This dissociation highlights a limitation of aggregated metrics and suggests that similar outputs may arise from non-equivalent system states. A fourth level is proposed as a purely conceptual extension describing potential relationships between system states. This level is not implemented and is not derived from experimental data. These observations are strictly exploratory and non-causal. The proposed framework does not establish mechanistic, predictive, or directional relationships, but provides a structured approach for analyzing constraint-driven systems across multiple levels of representation.

翻译：在生物力学系统中，可观察性能常被用作底层系统组织的代理指标。然而，这种假设隐含地假定了输出指标与内部系统状态之间存在对应关系，而这种对应关系在自适应系统中可能不成立。本研究将垂直咬合距离视为作用于自适应神经力学系统的约束条件，从而在受控变化条件下探索系统级响应。针对帕金森病患者的单案例设计允许在重复条件下进行个体内分析。分析架构包含三个互补层次：（i）描述可观察性能的聚合线性指标；（ii）描述状态空间时间组织的动力学系统框架；（iii）通过无监督嵌入获得的隐空间表征。结果表明，具有可比可观察性能的条件可能对应状态空间和隐空间表征中的不同组织。这种分离凸显了聚合指标的局限性，并提示相似输出可能源于非等效的系统状态。本文提出了第四个层次作为纯粹的概念性扩展，用于描述系统状态间的潜在关系。该层次尚未实现，也非源自实验数据。这些观察结果严格属于探索性且非因果性。所提出的框架未建立机制性、预测性或方向性关系，但为跨多表征层次分析约束驱动系统提供了结构化方法。