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Record W2588768903

Interference in sequential grasping: Effects of action and perception

2014· article· en· W2588768903 on OpenAlexaff
Kevin LeBlanc, David A. Westwood

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGRASPPerceptionAction (physics)Object (grammar)Task (project management)CommunicationPsychologyInterference (communication)Movement (music)Cognitive psychologyComputer scienceComputer visionMotion (physics)Artificial intelligenceEngineeringPhysicsAcoustics
DOInot available

Abstract

fetched live from OpenAlex

Past research has shown strong evidence supporting the notion that the visual control of action and the visual perception of objects are mediated by two functionally and anatomically distinct visual systems (Milner & Goodale, 2008). Little is known about how each visual system interferes with the other when performing a sequential task. However, it is known that the kinematics during the performance of an action can be interfered by a subsidiary perceptual task (Castiello, 1996). In the current study participants were presented with two rectangular objects placed one in front of the other. Participants were instructed to grasp the first object and place it on a specified target area and then either grasp (action condition) or make a perceptual judgment (perception condition) about the second object.  Participants’ peak grip aperture, movement time and reaction time to the first object were measured via 3D motion capture (OPTOTRAK 3020 system). The results revealed that preparing an action to the second object does not produce interference on the first action, but attending to its size for judgment does. Specifically, as the size of the second object increased, the amplitude of peak grip aperture and movement time towards the first object was also increased when performing the verbal perception condition. However, this pattern of interference was not shown when performing the action condition.

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)0.000
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 score0.917
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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