Cross-Domain Synthesis
Evidence-labelled connections between movement research, somatic knowledge and generative systems.
The Movement Is a Field, Not a Line: Motor Abundance and Generative Uncertainty
Connection type: Structural correspondence between task-stabilising variation in biological movement and multi-solution representations in generative motion AI. The motor-control evidence and the machine-learning evidence are each established in their own domains; the claim that practitioner-guided …
The Window of Now: Interpersonal Temporal Binding and the Latency Threshold of Responsive Generation
Connection type: Correspondence between a measured psychophysical constant (the temporal window of felt simultaneity) and an engineering threshold (real-time generation latency) — both governing when a response is experienced as "with me" rather than "after me"
Below the Skeleton: Muscle Redundancy and the Somatic Concept of Movement Quality as Convergent Discoveries
Connection type: Structural correspondence between a biomechanical property (muscle redundancy) and a somatic-pedagogical concept (movement quality) — independently established in both domains, now meeting at the muscle-level AI frontier
The Anticipatory Body: Predictive Coding in Nervous Systems and Generative AI as Convergent Architectures
Connection type: Structural homology between biological and computational anticipatory systems — convergent but not equivalent
The Resonance Benchmark: Kinesthetic Empathy as Evaluation Framework for Somatic-AI Motion Generation
When we watch a dancer fall, something in our own body registers the fall. This is not metaphor. The felt sense of witnessing movement — its pull on our own musculature, the micro-tensions it occasions in our spine and diaphragm — constitutes what phenomenologists call kinesthetic empathy: a mode of…
Weight, Yield, and the Shared Trajectory: Contact Improvisation as Epistemological Model for Human-AI Co-Generation
Contemporary human-AI interaction is structured, at its deepest architectural level, as command and response. Even the most sophisticated prompt-engineering frameworks presuppose a fundamental asymmetry: the human formulates intent, the model executes. The user is speaker; the system is interpreter.…