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Record W1987450526 · doi:10.1080/00222895.2012.747483

The Utility of Vision During Action: Multiple Visuomotor Processes?

2013· article· en· W1987450526 on OpenAlexafffund
Luc Tremblay, Steve Hansen, Andrew Kennedy, Darian T Cheng

Bibliographic record

VenueJournal of Motor Behavior · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaNipissing UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpulse (physics)PsychologyPhysical medicine and rehabilitationMotor controlAction (physics)Cognitive psychologyCommunicationComputer scienceNeuroscienceMedicinePhysics

Abstract

fetched live from OpenAlex

Recently, D. Elliott et al. (2010) asserted that the current control phase of a movement could be segregated in multiple processes, including impulse and limb-target regulation processes. The authors aimed to provide further empirical evidence and determine some of the constraints that govern these visuomotor processes. In 2 experiments, vision was presented or withdrawn when limb velocity was above or below selected velocity criteria. The authors observed that vision provided between 0.8 and 0.9 m/s significantly improved impulse regulation processes while vision provided up to 1.1 m/s significantly increased limb-target regulation processes. These results lend support to D. Elliott et al. and provide evidence that impulse regulation and limb-target regulation can take place at different velocities during a movement.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.304
Teacher spread0.258 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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