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Record W4391262688 · doi:10.1080/00222895.2024.2306322

Short-Term Modulation of Online Monocular Visuomotor Function

2024· article· en· W4391262688 on OpenAlexaff
Gabriela Oancea, Damian M. Manzone, Luc Tremblay

Bibliographic record

VenueJournal of Motor Behavior · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMonocularEye movementMovement (music)Term (time)NeuroplasticityPsychologyCommunicationComputer visionComputer scienceNeurosciencePhysics

Abstract

fetched live from OpenAlex

Previous literature suggests that correcting ongoing movements is more effective when using the dominant limb and seeing with the dominant eye. Specifically, individuals are more effective at adjusting their movement to account for an imperceptibly perturbed or changed target location (i.e., online movement correction), when vision is available to the dominant eye. However, less is known if visual-motor functions based on monocular information can undergo short-term neuroplastic changes after a bout of practice, to improve online correction processes. Participants (n = 12) performed pointing movements monocularly and their ability to correct their movement towards an imperceptibly displaced target was assessed. On the first day, the eye associated with smaller correction amplitudes was exclusively trained during acquisition. While correction amplitude was assessed again with both eyes monocularly, only the eye with smaller correction amplitudes in the pre-test showed significant improvement in delayed retention. These results indicate that monocular visuomotor pathways can undergo short-term neuroplastic changes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.308
Teacher spread0.260 · 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
Published2024
Admission routes1
Has abstractyes

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