Realizing the gravity of the simulation: adaptation to simulated hypogravity leads to altered predictive control.

biomechanics gravity adaptation locomotion motor control motor learning muscle preactivation

Journal

Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006

Informations de publication

Date de publication:
2024
Historique:
received: 06 03 2024
accepted: 06 05 2024
medline: 10 6 2024
pubmed: 10 6 2024
entrez: 10 6 2024
Statut: epublish

Résumé

Accurate predictive abilities are important for a wide variety of animal behaviors. Inherent to many of these predictions is an understanding of the physics that underlie the behavior. Humans are specifically attuned to the physics on Earth but can learn to move in other environments (e.g., the surface of the Moon). However, the adjustments made to their physics-based predictions in the face of altered gravity are not fully understood. The current study aimed to characterize the locomotor adaptation to a novel paradigm for simulated reduced gravity. We hypothesized that exposure to simulated hypogravity would result in updated predictions of gravity-based movement. Twenty participants took part in a protocol that had them perform vertically targeted countermovement jumps before (PRE), during, and after (POST) a physical simulation of hypogravity. Jumping in simulated hypogravity had different neuromechanics from the PRE condition, with reduced ground impulses (

Identifiants

pubmed: 38854629
doi: 10.3389/fphys.2024.1397016
pii: 1397016
pmc: PMC11157081
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1397016

Informations de copyright

Copyright © 2024 Rock, Kwak, Luo, Yang, Yun and Chang.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Chase G Rock (CG)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Samuel T Kwak (ST)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Angela Luo (A)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Xiao Yang (X)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Kristy Yun (K)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Young-Hui Chang (YH)

Comparative Neuromechanics Laboratory, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.

Classifications MeSH