Stabilizing Variables in Motor Control and Learning: A Control-Theoretic Account
Abstract
Motor learning is shaped by history, context, and task structure. Such phenomena are commonly explained using state-space models of adaptation or contextual-inference accounts, the latter proposing that inferred latent contexts or task states select or combine internal models or control policies. Here we propose a complementary control-theoretic account grounded in control feasibility.
We argue that effective motor control and learning may depend on the availability, construction, persistence, and decay of control-relevant variables: quantities available to or constructed within the controller that contribute to the generation, selection, or regulation of action through feedback, feedforward, or combined control pathways. Stabilizing variables form the subset of these control-relevant variables whose use within a controller supports stable and reliable regulation. This role is conditional, and the same variable may stabilize one controller or parameter regime yet be ineffective or destabilizing in another.
Using simplified plant-coupled simulations, we examine compensatory variables and dynamic states within feedback and feedforward architectures. Crossing force-bias and velocity-linked variables with constant-load and viscous perturbations shows that benefit depends on structural matching, while retained dynamic states produce aftereffect-like behavior after perturbation removal. An augmented-state analysis identifies a finite compensatory-gain boundary beyond which the constructed state destabilizes the closed loop. A curl-field reaching model shows how delay, noise, and contextual identifiability constrain compensation.
The framework provides a control-feasibility level of analysis. It suggests that effective motor control and learning may depend not only on error, reward, or contextual inference, but on whether variables are available, identifiable, timely, and compatible with stable control.
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BibTeX
@misc{howard2026stabilizing,
title = {{Stabilizing Variables in Motor Control and Learning: A Control-Theoretic Account}},
author = {Ian S. Howard},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/hs7bt_v2},
url = {https://osf.io/preprints/psyarxiv/hs7bt_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Stabilizing Variables in Motor Control and Learning: A Control-Theoretic Account
AU - Ian S. Howard
PY - 2026
DA - 2026/09/09
PB - PsyArXiv
DO - 10.31234/osf.io/hs7bt_v2
UR - https://osf.io/preprints/psyarxiv/hs7bt_v2/
AB - Motor learning is shaped by history, context, and task structure. Such phenomena are commonly explained using state-space models of adaptation or contextual-inference accounts, the latter proposing that inferred latent contexts or task states select or combine internal models or control policies. Here we propose a complementary control-theoretic account grounded in control feasibility. We argue that effective motor control and learning may depend on the availability, construction, persistence, and decay of control-relevant variables: quantities available to or constructed within the controller that contribute to the generation, selection, or regulation of action through feedback, feedforward, or combined control pathways. Stabilizing variables form the subset of these control-relevant variables whose use within a controller supports stable and reliable regulation. This role is conditional, and the same variable may stabilize one controller or parameter regime yet be ineffective or destabilizing in another. Using simplified plant-coupled simulations, we examine compensatory variables and dynamic states within feedback and feedforward architectures. Crossing force-bias and velocity-linked variables with constant-load and viscous perturbations shows that benefit depends on structural matching, while retained dynamic states produce aftereffect-like behavior after perturbation removal. An augmented-state analysis identifies a finite compensatory-gain boundary beyond which the constructed state destabilizes the closed loop. A curl-field reaching model shows how delay, noise, and contextual identifiability constrain compensation. The framework provides a control-feasibility level of analysis. It suggests that effective motor control and learning may depend not only on error, reward, or contextual inference, but on whether variables are available, identifiable, timely, and compatible with stable control.
ER -