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Record W2098528750 · doi:10.5539/ijps.v4n4p46

Hemispatial Effects for Left- and Right-handers on a Pointing Task

2012· article· en· W2098528750 on OpenAlexaffvenue
Pamela J. Bryden, Sara M. Scharoun Benson, Linda E. Rohr, Éric Roy

Bibliographic record

VenueInternational Journal of Psychological Studies · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of WaterlooMemorial University of NewfoundlandWilfrid Laurier University
Fundersnot available
KeywordsPsychologyKinematicsLeft handedTask (project management)Left and rightCognitive psychologySpace (punctuation)Object (grammar)Physical medicine and rehabilitationSocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The primary goal of the current study was to determine if left-handers show an advantage for each hand in its own region of space, as do right-handers. Additionally, the study aimed to determine whether a preferred-hand advantage for movement exists in a highly-practiced task. To examine these questions, 81 right- and 60 left-handers were administered the Waterloo Handedness Questionnaire (WHQ) and completed a computer-based pointing action, where kinematic data was recorded. Here, participants were required to move to a target, located to left, midline and right of the starting position, maximizing both speed and accuracy. A 3-target location (left, midline and right space) by two hand (left, right) repeated measures ANOVA was performed for each kinematic variable, for each handedness group separately. Results indicated that left-handers showed the same spatial compatibility or object proximity effect noted by other researchers in right-handers. However, no preferred-hand advantage was found, replicating the work of Bryden and Roy (1999) who showed that the existence of the preferred-hand advantage is dependent upon the degree of spatial precision required at the movement goal.

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)0.007
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 score0.041
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
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 head0.086
GPT teacher head0.425
Teacher spread0.339 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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