MétaCan
Menu
Back to cohort
Record W4412297277

Can listening to sound sequences facilitate movement? The potential for motor rehabilitation

2015· article· en· W4412297277 on OpenAlexaff
Rebeka Bodak, Lauren Stewart, Marianne A. Stephan, Maria A. G. Witek, Virginia B. Penhune, Peter Vuust

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsConcordia University
Fundersnot available
KeywordsActive listeningMovement (music)RehabilitationSound (geography)Physical medicine and rehabilitationComputer scienceSpeech recognitionPsychologyCommunicationAcousticsNeuroscienceMedicinePhysics
DOInot available

Abstract

fetched live from OpenAlex

One of the obstacles preventing patients from effective motor recovery following stroke is low frequency of sessions. What if listening to sounds between physical rehabilitation sessions could yield motor improvement? Our study provides a stepping stone to answering that question, by first examining the impact of auditory exposure on the formation of new motor memories in healthy nonmusicians. Following an audiomotor mapping session, participants will be asked to listen to and memorise sequence A or sequence B in a sound-only task. Employing a congruent/incongruent crossover design, participants’ motor performance will be tested using visuospatial stimuli to cue key presses, either to the congruent sequence they heard, or to the incongruent unfamiliar sequence. It is predicted that the congruent group will perform faster than the incongruent group. The findings of this study have the potential to be useful in motor rehabilitation settings where the coupling of sound and movement patterns might help patients relearn motor tasks relevant to activities of daily living, particularly when regular physical practice is not possible.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.073
GPT teacher head0.335
Teacher spread0.262 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicAction Observation and SynchronizationFrench-language works237,207