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Record W2609474644 · doi:10.1080/20445911.2017.1310108

Stimulus size matters: do life-sized stimuli induce stronger embodiment effects in mental rotation?

2017· article· en· W2609474644 on OpenAlexaff
Sandra Kaltner, Petra Jansen, Bernhard E. Riecke

Bibliographic record

VenueJournal of Cognitive Psychology · 2017
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmbodied cognitionPsychologyMental rotationStimulus (psychology)Cognitive psychologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Against the background of the embodied cognition approach this experiment investigated the influence of motor expertise on object-based vs. egocentric transformations in a chronometric mental rotation (MR) task using images of either the own or another person’s body as stimulus material. The present study aimed to clarify two issues: (1) whether stimulus size (life size vs. small) is able to induce embodiment effects and (2) which role self-awareness processes play when using stimuli of the own body. The same design was conducted twice using both small stimuli (Study 1) and life-size human figures (Study 2). Using life-sized figures in Study 2 resulted in an explicit advantage of self-related stimuli and improved performance for motor experts compared to non-motor experts in both object-based and egocentric transformations. In conclusion, these results suggest that life-sized figures do indeed induce stronger embodiment effects in MR.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.340
Teacher spread0.319 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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