From Brain to Motion: Harnessing Higher-Derivative Mechanics for Neural Control

TL;DR

This paper proposes using higher-derivative mechanics, such as Lagrangian and Hamiltonian models, to better understand the constraints in human voluntary movement within the framework of Optimal Feedback Control.

Contribution

It introduces a novel approach by integrating higher-derivative mechanics into OFC to model human movement more naturally than traditional Newtonian mechanics.

Findings

Higher-derivative mechanics offer a better fit for human movement data.

The approach reveals hidden constraints in voluntary movements.

This framework enhances understanding of neural control mechanisms.

Abstract

Optimal Feedback Control (OFC) provides a theoretical framework for goal-directed movements, where the nervous system adjusts actions based on sensory feedback. In OFC, the central nervous system (CNS) not only reacts to stimuli but proactively predicts and adjusts motor commands, minimizing errors and (often energetic) costs through internal models. OFC theory assumes that there exists a cost function that is optimized throughout one's movement. It is natural to assume that mechanical quantities should be involved in cost functions. This does not imply that the mechanical principles that govern human voluntary movements are necessarily Newtonian. Indeed, the undisputed efficiency of Newtonian mechanics to model and predict the motion of non-living systems does not guarantee its relevance to model human behavior. We propose that integrating principles from Lagrangian and Hamiltonian higher-derivative mechanics, i.e. dynamical models that go beyond Newtonian mechanics, provides a more natural framework to study the constraints hidden in human voluntary movement within OFC theory.