Evidence map›Paper›PMID 41969150›Full record

ReviewThe Journal of experimental biology2026

Mechanical control: parallel pathways for modulating movement.

Crystal M Reynaga, Emanuel Azizi

Abstract readReview
In one paragraph

Review in The Journal of experimental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Crystal M ReynagaDepartment of Evolution, Ecology, and Organismal Biology, University of California, Riverside, Riverside, CA 92521, USA.ORCID 0000-0003-3264-5658
Emanuel AziziDepartment of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697-2525, USA.ORCID 0000-0001-7338-8673

Funding

Elastic mechanisms in locomotionR01AR055295 · NIAMS · BROWN UNIVERSITY · PI EMANUEL AZIZI, Thomas Jay Roberts · 2008 to 2026
$6.0M
Division of Biological Infrastructure NSF-DBI 2319710National Science Foundation Division of Integrative Organismal Systems DBI-2514564NIAMS NIH HHS R01AR055295University of California, Riverside
6 · The paper itself

Abstract

Animals across diverse taxa navigate complex and unpredictable environments by modulating movement to maintain stability and efficiency. Active neural feedback has traditionally been viewed as the sole and primary mechanism for control. In this Review, we highlight the importance of non-neural, mechanical control mechanisms that allow for rapid modulation of locomotor systems. As the speed of movement increases or organisms face perturbations that rapidly change loading conditions, neural responsiveness may become too slow to adapt effectively. In such cases, pre-tuned modulation through intrinsic muscle properties, elastic structures and tissue compliance can provide a faster, more reliable response, sometimes outperforming active control in maintaining stability and energetic efficiency. We explore how these forms of mechanical control complement neural feedback and enhance control across size scales, particularly in systems where rapid adjustments are critical. We synthesize recent findings to provide a framework for understanding the trade-offs between passive and active control and highlight the potential mechanisms that function in parallel, across levels of biological organization to modulate locomotor output.

Indexed as

LocomotionAnimalsBiomechanical PhenomenaEmbodied intelligenceIntrinsic propertiesMechanical controlModulationPassive mechanismsStructural control

Identifiers

PMID41969150
PMCPMC13120678

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.