Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
26
11
2020
accepted:
05
05
2021
revised:
23
06
2021
pubmed:
12
6
2021
medline:
14
10
2021
entrez:
11
6
2021
Statut:
epublish
Résumé
Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.
Identifiants
pubmed: 34115757
doi: 10.1371/journal.pcbi.1009047
pii: PCOMPBIOL-D-20-02130
pmc: PMC8221793
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1009047Subventions
Organisme : NINDS NIH HHS
ID : R21 NS120274
Pays : United States
Organisme : NINDS NIH HHS
ID : R56 NS100528
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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