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Record W2051659230 · doi:10.1093/neucas/8.3.194

Selective, Non-lateralized Impairment of Motor Imagery Following Right Parietal Damage

2002· article· en· W2051659230 on OpenAlexaff
James Danckert, Susanne Ferber, Timothy J. Doherty, Helena Steinmetz, David A. Nicolle, Melvyn A. Goodale

Bibliographic record

VenueNeurocase · 2002
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsWestern University
Fundersnot available
KeywordsContrast (vision)Parietal lobePsychologyMotor imageryTask (project management)NeglectMovement (music)Cognitive psychologyPerceptionAudiologyPosterior parietal cortexPhysical medicine and rehabilitationArtificial intelligenceNeuroscienceComputer scienceElectroencephalographyMedicineBrain–computer interface

Abstract

fetched live from OpenAlex

Using variants of a visually guided pointing task, in which subjects make pointing movements towards targets of varying sizes, we explored motor imagery in a patient with visual neglect. When this patient actually pointed towards targets of different sizes he showed the normal correlation between movement duration (MD) and target size, such that MD increased as target size decreased. In contrast, his imagined movements did not show the same speed-accuracy trade-off observed for actual movements. This was true regardless of the hand used or the initial direction of movement (left versus right). The patient performed normally on several tasks of visual imagery, including size estimation, perceptual discrimination and localization of cities on an imagined map. This patient's performance suggests that the networks in the right parietal lobe play an important role in the generation of internal models of motor movements regardless of the hand used to perform the task.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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 designCase report
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

Citations73
Published2002
Admission routes1
Has abstractyes

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