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Guiding Movements with Internal Representations: A Reach-and-Grasp Task

2003· article· en· W2333308203 on OpenAlexaff
Alissa D. Fourkas, Ronald G. Marteniuk, Michael A. Khan

Bibliographic record

VenueResearch Quarterly for Exercise and Sport · 2003
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMovement (music)GRASPTask (project management)Prehensile tailVisual feedbackComputer visionPhysical medicine and rehabilitationCommunicationPsychologyComputer scienceArtificial intelligenceCognitive psychologyMedicineEngineeringPhysics

Abstract

fetched live from OpenAlex

We investigated participants' ability to use internal representations of the environment to guide prehensile movements, when visual feedback was not available. Reaching and grasping performed with concurrent visual feedback was compared to conditions in which participants actively formed spatial images and passively encoded images from visual presented information. Movement times, the proportion of time spent after peak velocity and peak apertures, were greater when concurrent visual feedback was unavailable. Movement times increased as a function of premovement occlusion length, with passively encoded images resulting in shorter movement durations than actively formed images. The findings indicated that participants adapted their movement trajectories to compensate for the degradation of stored spatial information, when concurrent visual feedback was not available.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.349
Teacher spread0.270 · 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 designBench or experimental
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

Citations6
Published2003
Admission routes1
Has abstractyes

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