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 this http URL 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.
| Comments: | 1 table, 4 figures. Exploratory single-case study |
| Subjects: | Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2605.00778 [cs.LG] |
| (or arXiv:2605.00778v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00778 arXiv-issued DOI via DataCite (pending registration) |
From: Jacques Raynal [view email]
[v1]
Fri, 1 May 2026 16:48:00 UTC (3,627 KB)
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