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Record W4404707837 · doi:10.1371/journal.pone.0314270

Sex differences persist in visuospatial mental rotation under 3D VR conditions

2024· article· en· W4404707837 on OpenAlexafffund
Oliver Jacobs, Katerina Andrinopoulos, Jennifer K. E. Steeves, Alan Kingstone

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsYork UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaFederation for the Humanities and Social Sciences
KeywordsMental rotationVirtual realitySpatial abilityPsychologyAdaptation (eye)Cognitive psychologyCognitionComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

The classic Vandenberg and Kuse Mental Rotations Test (MRT) shows a male advantage for visuospatial rotation. However, MRTs that have been adapted for use with real or physical objects have found that sex differences are reduced or abolished. Previous work has also suggested that virtual 3D objects will eliminate sex differences, although this has not been demonstrated in a purely visuospatial paradigm without motor input. In the present study we sought to examine potential sex differences in mental rotation using a fully-immersive 3D VR adaptation of the original MRT that is purely visuospatial in nature. With unlimited time 23 females and 23 males completed a VR MRT designed to approximate the original Vandenberg and Kuse stimuli. Despite the immersive VR experience and lack of time pressure, we found a large male performance advantage in response accuracy, exceeding what has typically been reported for 2D MRTs. No sex differences were observed in response time. Thus, a male advantage in pure mental rotation for 2D stimuli can extend to 3D objects in VR, even when there are no time constraints.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.037
GPT teacher head0.240
Teacher spread0.203 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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