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Record W1828735200 · doi:10.1111/mbe.12051

Mental Rotation With Tangible Three‐Dimensional Objects: A New Measure Sensitive to Developmental Differences in 4‐ to 8‐Year‐Old Children

2015· article· en· W1828735200 on OpenAlexaff
Zachary Hawes, Jo‐Anne LeFevre, Chang Xu, Catherine D. Bruce

Bibliographic record

VenueMind Brain and Education · 2015
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsTrent UniversityCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsMental rotationMeasure (data warehouse)Rotation (mathematics)PsychologyTest (biology)Spatial abilityCognitive psychologyMental developmentDevelopmental psychologyCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT There is an emerging consensus that spatial thinking is fundamental to later success in math and science. The goals of this study were to design and evaluate a novel test of three‐dimensional (3D) mental rotation for 4‐ to 8‐year‐old children (N = 165) that uses tangible 3D objects. Results revealed that the measure was both valid and reliable and indicated steady growth in 3D mental rotation between the ages of 4 and 8. Performance on the measure was highly related to success on a measure of two‐dimensional (2D) mental rotation, even after controlling for executive functioning. Although children as young as 5 years old performed above chance, 3D mental rotation appears to be a difficult skill for most children under the age of 7, as indicated by frequent guessing and difficulty with mirror objects. The test is a useful new tool for studying the development of 3D mental rotation in young children.

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)0.004
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations106
Published2015
Admission routes1
Has abstractyes

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