Volume IV: Human Motor Control
Volume IV examines hypotheses and models for high-dimensional human movement. This page publishes the current chapter map and source links in articles/The_Geometry_of_Motion/Volume_IV/.
Publication state: available as a provisional research-development manuscript; not validated as a neural mechanism, clinical account, human-performance model, or deployable robot-control specification.
Scientific Status
This volume is an active computational neuroscience and motor-control manuscript. Claims are presented for iterative testing and traceable refinement rather than as settled doctrine.
Source Traceability
- Primary manuscript source:
articles/The_Geometry_of_Motion/Volume_IV/main.tex - Chapter source files:
articles/The_Geometry_of_Motion/Volume_IV/chapters/*.tex - Notebook bridge manifest:
notebooks/geometry_of_motion/manifest.json
Notebook Workflow
The bridge manifest labels the listed Jupyter notebooks scaffolded. That state means a file and tutorial title exist; it does not establish numerical correctness, successful execution, dependency availability, or reproducibility. The revision-pinned GitHub and Colab links below preserve the reviewed source snapshot; Colab execution still depends on an external runtime.
Chapter 1: Degrees-of-Freedom Problem
Frames the Bernstein problem and why naive control decompositions fail.
Chapter 2: Curse of Dimensionality
Explains complexity barriers and biologically grounded reduction strategies.
Chapter 3: Neural Architecture
Introduces structural organization principles behind movement generation.
Chapter 4: Shallow-but-Wide Networks
Explores parallelism-first computation as a candidate motor-control model; no timing benchmark or neural validation is asserted.
Chapter 5: Ideomotor Theory
Connects perception-action coupling to policy structure and learning.
Chapter 6: Internal Models
Forward and inverse model architectures for prediction and control.
Chapter 7: Passive Distributed Control
Discusses models in which passive mechanics and active control can share stabilizing roles under stated assumptions.
Additional Canonical Source: Chapter 7B, the Interplay of Biology and Dynamics of Nonlinear Systems
Presents the manuscript’s nonlinear biology-dynamics synthesis as a hypothesis-building chapter rather than validated causal evidence.
Chapter 8: Central Pattern Generation
Covers rhythmic controllers and their modulation for adaptive movement.
Chapter 9: Motor Learning and Adaptation
Treats error-driven adaptation and retention in biological control.
Chapter 10: Computational Models
Compares candidate neural-control models conceptually; no empirical performance benchmark is asserted by this route.
Chapter 11: From Neural Principles to Robot Control
Discusses hypotheses for translating biological motor principles into robot-control research architectures; deployment readiness is not established.