MétaCan
Menu
Back to cohort
Record W2886665249 · doi:10.1002/brb3.1025

Hand selection in a preferential reaching task: The effects of object location, orientation, and task intention in preadolescent children

2018· article· en· W2886665249 on OpenAlexaff
Sara M. Scharoun Benson, Amanda Forsyth, Pamela J. Bryden

Bibliographic record

VenueBrain and Behavior · 2018
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsWilfrid Laurier UniversityUniversity of Windsor
Fundersnot available
KeywordsObject (grammar)Task (project management)Selection (genetic algorithm)Orientation (vector space)PsychologyCognitive psychologyPreferenceObject-orientationHand preferenceDevelopmental psychologyComputer scienceArtificial intelligenceLateralityObject-oriented programmingMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION AND METHODS: Hand selection was assessed in preadolescent children (ages 9-11) within a preferential reaching task to delineate the effects of object location, orientation, and task intention on the assessment procedure and compared to data previously acquired from young adults. RESULTS: The observed differences support the notion that children are still in a process of refining their movements in attempt to discern the most efficient and effective patterns of behavior. Notwithstanding differences in performance, similarities between preadolescents and young adults also emerged. Greater right-hand selection in right space and when the handle was oriented to the right indicate that object proximity and orientation influence efficiency and thus constrain hand selection in unimanual object manipulation and role-differentiated bimanual manipulation. CONCLUSIONS: Together, findings add to our understanding of hand preference, unimanual and bimanual object manipulation.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.270
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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBrain and BehaviorSame topicHemispheric Asymmetry in NeuroscienceFrench-language works237,207