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Record W3131845809 · doi:10.1002/dev.22112

How far will you go before switching hands? Handedness on the long pegboard across the lifespan

2021· article· en· W3131845809 on OpenAlexafffund
Sara M. Scharoun Benson, Nicole Williams, Jessica A. L. Tucker, Pamela J. Bryden

Bibliographic record

VenueDevelopmental Psychobiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsWilfrid Laurier UniversityUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLateralityPsychologyAudiologyHand preferenceLeft handedYoung adultDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Handedness is a significant behavioral asymmetry; however, there is debate surrounding the age at which hand preference develops, and little research has been conducted on handedness in older adults. The current study examined performance on the long pegboard, to identify similarities and differences in young children (ages 4-7 years), older children (ages 8-12 years), young adults (ages 18-25 years), and older adults (ages 70+ years). Average time per hole, number of hand switches, and errors were assessed with left- and right-hand starts. A left-right ratio was computed from the long pegboard, along with laterality quotients from the Waterloo Handedness Questionnaire (WHQ). Results revealed faster performance when participants started the task on the right side of the long pegboard with the right-hand, coupled with a later switch to the left-hand. There was a greater number of errors with left-hand starts, and an earlier switch to the right-hand. Age was a significant predictor of the average time per hole and number of errors. Long pegboard ratio and WHQ laterality quotient were only correlated for adults. Together, findings offer insight regarding age-related effects in handedness and support the long pegboard as a useful measure of handedness.

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 categoriesScience and technology studies
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 score0.149
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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.

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
Published2021
Admission routes2
Has abstractyes

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