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Record W2013128513 · doi:10.1097/wnr.0b013e3281ac2143

Shared and differential neural substrates of copying versus drawing: a functional magnetic resonance imaging study

2007· article· en· W2013128513 on OpenAlexaff
Susanne Ferber, Richard Mraz, Nicole M. Baker, Simon J. Graham

Bibliographic record

VenueNeuroreport · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsHealth Sciences CentreUniversity of TorontoHeart and Stroke FoundationSunnybrook Health Science CentreBaycrest Hospital
Fundersnot available
KeywordsFunctional magnetic resonance imagingCopyingNeurosciencePsychologyCognitionFunctional imagingMagnetic resonance imagingCuneusCrossmodalVisual perceptionCognitive psychologyPerceptionMedicineBiology

Abstract

fetched live from OpenAlex

Copying and drawing-from-memory tasks are popular clinical tests to assess visuo-motor skills in neurological patients. The tasks share some motor and visual processes; however, they differ substantially in their cognitive demands. We used functional magnetic resonance imaging to identify brain regions underlying processes involved in these tasks while avoiding confounds related to basic motor requirements, through use of a specially developed functional magnetic resonance imaging-compatible computer tablet. For the copying task, activation was observed in brain regions subserving visual processing and crossmodal attention (e.g. left lingual gyrus, cuneus). Drawing activated the anterior cingulate, an area associated with motor control and linking intention with action. These findings suggest distinct neural networks subserving copying and drawing.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.276
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 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

Citations42
Published2007
Admission routes1
Has abstractyes

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