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Record W2944030005

An electroencephalographic analysis of mental visuomotor rotation

2012· article· en· W2944030005 on OpenAlexaff
Stephane MacLean, Cameron D. Hassall, Matthew Heath, Olav Krigolson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsWestern UniversityDalhousie University
Fundersnot available
KeywordsMental rotationPsychologyRotation (mathematics)CognitionCognitive psychologyElectroencephalographyMovement (music)Event-related potentialCommunicationDevelopmental psychologyNeuroscienceArtificial intelligenceComputer sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

Recent experimental evidence (Neely & Heath, 2010) suggests that visuomotor mental rotation does not rely on a rotation process that occurs just prior to response initiation, but instead relies on a more complex and cognitive substitution process related to the sensorimotor transformations mediating the response. Here, we sought to provide further evidence for this contention by using event-related brain potentials (ERP) to demonstrate that an increase in the degree of visuomotor mental rotation resulted in a related increase in the amplitude of the P300 – an ERP component that has an amplitude sensitive to the magnitude of cognitive processing. Our behavioral results are in line with previous work – the amount of spherical variability in movement endpoints increased in relation to increases in the degree of visuomotor mental rotation. Not surprisingly, our ERP data revealed differences in visual processing between the presentation of unrotated and rotated targets. However, as predicted, the amplitude of the P300 scaled to the degree of visuomotor mental rotation – a result that supports the Neely and Heath's findings and suggests that visuomotor mental rotation relies on a cognitive response substitution process as opposed to a rotation of the movement vector just prior to movement onset.

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 score0.613
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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