Observable Performance Does Not Fully Reflect System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
2026-05-01 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied how a person with Parkinson's disease adapts to changes in the vertical dimension of occlusion (VDO), which means how the mouth opens and closes. They found that even when the person's outward performance looks similar, the internal brain and muscle system can be organized in very different ways. This means simple performance measures might not show the full picture of what's happening inside the body. The authors suggest a new way to analyze these differences by looking at multiple layers of system behavior, but their study is exploratory and does not claim cause and effect.
Vertical Dimension of OcclusionNeuromechanical SystemsAdaptive SystemsState SpaceLatent SpaceParkinson's DiseaseDynamical SystemsUnsupervised EmbeddingSystem OrganizationBiomechanics
Authors
Jacques Raynal, Pierre Slangen, Jacques Margerit
Abstract
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.