Evidence map›Paper›PMID 39489737›Full record

ArticleNature communications2024

Exploration-based learning of a stabilizing controller predicts locomotor adaptation.

Nidhi Seethapathi, Barrett C Clark, Manoj Srinivasan

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Review
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  8. Gait Adaptation to Asymmetric Foot-Ground Compliance Applied by Robotic Footwear.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
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  9. Article
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  11. Article
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  14. Foot placement control underlies stable locomotion across species.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Nidhi SeethapathiDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. nidhise@mit.edu.ORCID 0000-0002-5159-9717
Barrett C ClarkBright Machines, Inc., San Francisco, CA, USA.
Manoj SrinivasanDepartment of Mechanical and Aerospace Engineering, the Ohio State University, Columbus, OH, USA.ORCID 0000-0002-7811-3617

Funding

Collaborative Research: A Predictive Theory of Muscle Energy Consumption R01GM135923 · NIGMS · WORCESTER POLYTECHNIC INSTITUTE · PI WALCOTT, SAMUEL · 2019 to 2022
$1.5M
National Science Foundation (NSF) 2014506NIGMS NIH HHS R01 GM135923U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01GM135923
6 · The paper itself

Abstract

Humans adapt their locomotion seamlessly in response to changes in the body or the environment. It is unclear how such adaptation improves performance measures like energy consumption or symmetry while avoiding falling. Here, we model locomotor adaptation as interactions between a stabilizing controller that reacts quickly to perturbations and a reinforcement learner that gradually improves the controller's performance through local exploration and memory. This model predicts time-varying adaptation in many settings: walking on a split-belt treadmill (i.e. with both feet at different speeds), with asymmetric leg weights, or using exoskeletons - capturing learning and generalization phenomena in ten prior experiments and two model-guided experiments conducted here. The performance measure of energy minimization with a minor cost for asymmetry captures a broad range of phenomena and can act alongside other mechanisms such as reducing sensory prediction error. Such a model-based understanding of adaptation can guide rehabilitation and wearable robot control.

Indexed as

Adaptation, PhysiologicalLearningLocomotionWalkingAdultBiomechanical PhenomenaFemaleGaitHumansMaleRoboticsYoung Adult

Identifiers

PMID39489737
PMCPMC11532365

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.