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Record W2035044849 · doi:10.1080/00222895.2012.740098

Motor Imagery and Aging

2013· review· en· W2035044849 on OpenAlexafffund
Arnaud Saimpont, Francine Malouin, Béatrice Tousignant, Philip L. Jackson

Bibliographic record

VenueJournal of Motor Behavior · 2013
Typereview
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsMotor imageryPsychologyRetrainingNeurostimulationPhysical medicine and rehabilitationRehabilitationMotor functionCognitive psychologyAction (physics)NeuroimagingMental imageRepetition (rhetorical device)CognitionMedicineNeuroscienceElectroencephalographyBrain–computer interface

Abstract

fetched live from OpenAlex

Motor imagery (MI) is the mental simulation of an action without its actual execution. It has been successfully used through mental practice--the repetition of imagined movements--to optimize motor function either in sport or rehabilitation settings. Healthy elderly individuals facing age-related impairments in motor function could also benefit from this method of training-retraining. The authors review studies that have investigated MI in physically and mentally healthy adults aged 55 years and older. First, they provide an overview of the psychophysical data on MI in the elderly, which show no changes with aging in the ability to imagine simple-usual movements but reveal some age-related alterations in the mental simulation of difficult-unusual movements. Second, they present emerging neuroimaging and neurostimulation data revealing that the sensorimotor system is engaged during MI in older adults. Finally, the authors emphasize the potential of using mental practice as a safe and easy way to help preserving/improving motor function in the elderly and provide some recommendations for future research in this direction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.421
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations89
Published2013
Admission routes2
Has abstractyes

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