MétaCan
Menu
Back to cohort
Record W1982614795 · doi:10.1027/1614-0001/a000020

Ecological Aspects of Mental Rotation Around the Vertical and Horizontal Axis

2010· article· en· W1982614795 on OpenAlexaff
Christian Battista, Michael Peters

Bibliographic record

VenueJournal of Individual Differences · 2010
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVertical axisHorizontal axisHorizontal and verticalRotation (mathematics)Mental rotationContext (archaeology)Orientation (vector space)GeometryCube (algebra)Long axisGeodesyPsychologyPhysicsGeologyMathematicsEngineeringStructural engineeringPaleontology

Abstract

fetched live from OpenAlex

Rotation of both natural and man-made objects most commonly requires rotation around the vertical rather than the horizontal axis because it is relatively rare that we need to rotate, e.g., trees, mountains, chairs or vehicles around their horizontal axis in order to match images to their canonical orientation. Waszak, Drewing, and Mausfeld (2005) demonstrated the importance of a gravitationally defined vertical axis and the visual context within which objects occur, when performing mental rotations. We extended their findings in a between-subject design by asking 406 subjects to rotate wireframe cube figures around either the vertical axis or around the horizontal axis. Both male and female subjects performed significantly better when rotating objects around the vertical axis. Males performed better than females in both conditions, and there was no interaction between axis of rotation and sex.

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)0.000
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.243
Teacher spread0.225 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Individual DifferencesSame topicSpatial Cognition and NavigationFrench-language works237,207