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.
View Reviewed Main Source Manuscript Read the Reviewed Volume PDF
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. Manuscript and PDF links pin the technically reviewed edition dated 5 October 2026. The revision-pinned notebook and Colab links retain historical scaffolds; Colab execution still depends on an external runtime. Chapter 7B’s reviewed source and the volume PDF contain the revised biology treatment described below.
Chapter 1: Degrees-of-Freedom Problem
Connects task geometry, muscle-force allocation and dynamic feasibility. The chapter distinguishes exact task-preserving configurations from their tangent approximation, and explains what repeated-movement variability can reveal about control. Its reaching example has executable checks and deliberately constructed data; it is not evidence of a human neural mechanism.
The notebook below retains its historical scaffold snapshot and has not received this chapter’s numerical review.
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
Examines when a shorter computational path can help a delayed controller. The corrected chapter distinguishes anatomical layers from processing stages, response latency from update frequency, and EMG reconstruction from evidence for a neural mechanism. Its network and factorization examples have executable numerical checks; their assumed delays are not biological measurements or control-performance benchmarks.
The notebook below retains its historical scaffold snapshot and has not received this chapter’s numerical review.
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
Connects muscle–tendon mechanics, activation history, coordination, task geometry and feedback. Worked examples distinguish orbit stability from timing accuracy, muscle synergies from proof of a neural mechanism, and local tissue response from stability of the coupled body. The golf and humanoid experiment ladder links those distinctions to delivered tool state, contact loads and measurable outcomes.
The chapter’s mathematical examples have independent numerical and symbolic checks. Human causal and performance claims remain hypotheses requiring the stated measurements; this correction does not validate the entire volume.
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.
Follow-Up and Challenge Paths
Citation
@online{untitled,
author = {},
title = {Volume {IV:} {Human} {Motor} {Control}},
url = {https://affinedrift.com/books/human-motor-control.html},
langid = {en}
}