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Record W3142716921 · doi:10.1177/0956797620975774

Using Body Ownership to Modulate the Motor System in Stroke Patients

2021· article· en· W3142716921 on OpenAlexaff
Riccardo Tambone, Alberto Giachero, Melanie Calati, Maria Teresa Molo, Dalila Burin, Maria Pyasik, Francesca Cabria, Lorenzo Pia

Bibliographic record

VenuePsychological Science · 2021
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsIllusionAvatarPsychologyPerspective (graphical)Physical medicine and rehabilitationBalance (ability)Motor learningMotor systemStroke (engine)Cognitive psychologyMedicineNeuroscienceHuman–computer interactionComputer science

Abstract

fetched live from OpenAlex

Recent findings suggest that body ownership can activate the motor system in the absence of movement execution. Here, we investigated whether such a process promotes motor recovery in stroke patients. A group of patients with left-hemisphere damage ( N = 12) and chronic motor deficits completed an immersive virtual reality training (three sessions of 15 min each week for 11 weeks). Patients sat still and either experienced (first-person perspective) or did not experience (third-person perspective) illusory ownership over the body of a standing virtual avatar. After the training, in which the avatar walked around a virtual environment, only patients who experienced the illusion improved gait and balance. We argue that representing the virtual body as their own allowed patients to access motor functioning and promoted motor recovery. This procedure might be integrated with rehabilitative approaches centered on motor execution. These findings also have an impact on the knowledge of the motor system in general.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.394
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations31
Published2021
Admission routes1
Has abstractyes

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