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Record W3012707342 · doi:10.1037/cep0000205

Sex differences in curve tracing.

2020· article· en· W3012707342 on OpenAlexfundno aff
Daniel Voyer, Benjamin R. MacPherson

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2020
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsycINFOMental rotationTracingPsychologyTask (project management)Cognitive psychologyPreferenceSocial psychologyStatisticsCognitionComputer scienceMathematicsEngineering

Abstract

fetched live from OpenAlex

The present study reports on 4 experiments aimed at investigating potential sex differences on a curve tracing task. Furthermore, curve tracing was used as an indirect approach to explore the holistic versus piecemeal strategy hypothesis used to account for sex differences in mental rotation. In Experiment 1, participants only completed a curve tracing task. The Navon (1977) local/global task was added in Experiment 2, whereas mental rotation was included in Experiment 3. Experiment 4 corrected issues encountered with the mental rotation task in Experiment 3. All 4 experiments showed a performance advantage for men on accuracy in curve tracing, although the Sex × Distance interaction required to support preference for a holistic strategy in men was not found. The Navon task findings supported the notion that men show a reduced global precedence effect when compared with women. The performance advantage for men in mental rotation only emerged in Experiment 4. Finally, the tasks showed a pattern of correlations suggestive of common components aside from attention. The General Discussion focuses on alternative explanations of the findings and further research required to elucidate them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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 categoriesMeta-epidemiology (narrow)
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 score0.159
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.056
GPT teacher head0.282
Teacher spread0.226 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

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