Competition Between Task Accuracy and Habitual Locomotor Behavior Underlies the Speed-Accuracy Trade-Off in Walking

The speed-accuracy trade-off is a hallmark feature of motor behavior, yet its underlying causes in cyclic, whole-body movements such as walking remain to be understood. In discrete, point-to-point movements, reduced accuracy at higher speeds is typically attributed to signal-dependent motor noise and an inability to use online error correction. Walking, however, may be governed by additional objectives, such as minimizing metabolic energy expenditure. To distinguish between these mechanisms, we tested young adults in a precision-walking task requiring them to match target step lengths across a range of treadmill speeds. Accuracy declined systematically as walking speed increased, demonstrating a locomotor speed-accuracy trade-off. Contrary to prevailing theory, removing online visual feedback of foot position did not affect this decline, indicating little contribution from visually guided error correction. Moreover, step-length variability, a task-specific measure of signal-dependent motor noise, decreased rather than increased with speed. Instead, faster walking produced progressively larger biases toward participants' habitual step lengths measured in the absence of targets. Because habitual step lengths are known to closely approximate energetically optimal values, these findings suggest energetic optimization, rather than motor noise or limitations associated with the use of online feedback, as the primary driver of the speed-accuracy trade-off during walking.

Authors

Institutions

Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-26
DOI
https://doi.org/10.64898/2026.10.21.760535
Primary Topic
Motor Control and Adaptation
Type
preprint

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Competition Between Task Accuracy and Habitual Locomotor Behavior Underlies the Speed-Accuracy Trade-Off in Walking

Nicolas Schweighofer, Isaiah J. Lachica, James M. Finley
bioRxiv (Cold Spring Harbor Laboratory)
Motor Control and Adaptation
preprint

Competition Between Task Accuracy and Habitual Locomotor Behavior Underlies the Speed-Accuracy Trade-Off in Walking

Nicolas Schweighofer, Isaiah J. Lachica, James M. Finley
preprint en

Abstract

The speed-accuracy trade-off is a hallmark feature of motor behavior, yet its underlying causes in cyclic, whole-body movements such as walking remain to be understood. In discrete, point-to-point movements, reduced accuracy at higher speeds is typically attributed to signal-dependent motor noise and an inability to use online error correction. Walking, however, may be governed by additional objectives, such as minimizing metabolic energy expenditure. To distinguish between these mechanisms, we tested young adults in a precision-walking task requiring them to match target step lengths across a range of treadmill speeds. Accuracy declined systematically as walking speed increased, demonstrating a locomotor speed-accuracy trade-off. Contrary to prevailing theory, removing online visual feedback of foot position did not affect this decline, indicating little contribution from visually guided error correction. Moreover, step-length variability, a task-specific measure of signal-dependent motor noise, decreased rather than increased with speed. Instead, faster walking produced progressively larger biases toward participants' habitual step lengths measured in the absence of targets. Because habitual step lengths are known to closely approximate energetically optimal values, these findings suggest energetic optimization, rather than motor noise or limitations associated with the use of online feedback, as the primary driver of the speed-accuracy trade-off during walking.

bioRxiv (Cold Spring Harbor Laboratory)
University of Southern California (US)
National Science Foundation
Motor Control and Adaptation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon