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Record W2081606688 · doi:10.1080/1357650x.2010.514344

Under what conditions will right-handers use their left hand? The effects of object orientation, object location, arm position, and task complexity in preferential reaching

2011· article· en· W2081606688 on OpenAlexaff
Pamela J. Bryden, Justine Huszczynski

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2011
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsObject (grammar)Orientation (vector space)Position (finance)Task (project management)VersaLeft and rightLeft handedObject-orientationHand positionPsychologySelection (genetic algorithm)Cognitive psychologyComputer visionArtificial intelligenceCommunicationComputer scienceMathematicsGeometryEngineeringObject-oriented programmingPhysics

Abstract

fetched live from OpenAlex

When reaching to objects, it is known that the preferred hand is selected significantly more often for midline and ipsilateral reaches, although right-handers are more likely to continue to use their right hand to reach for an object in contralateral space (Mamolo, Roy, Rohr, & Bryden, 2006). The current study examined the influence of object orientation, object location, task complexity, and initial position of the hands on a reaching task in a sample of 45 right-handed adults. Participants reached to and picked up a mug oriented in one of three ways (handle to right, left, or neutral position) located in one of three spatial positions (left, right, and midline) from each of two starting hand positions: right hand over left, and vice versa. Along with the expected results, an interesting pattern emerged where both the orientation of the object and the position of the hands had significant effects on hand selection, such that the use of the non-preferred left hand was augmented in conditions where preferred-hand use was awkward and biomechanically inefficient. The results will be discussed in light of current theories accounting for hand selection preferences.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.002
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 head0.041
GPT teacher head0.269
Teacher spread0.229 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

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