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Direct effects of prismatic lenses on visuomotor control: an event‐related functional MRI study

2008· article· en· W1973141622 on OpenAlexafffund
James Danckert, Susanne Ferber, Melvyn A. Goodale

Bibliographic record

VenueEuropean Journal of Neuroscience · 2008
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsWestern UniversityUniversity of TorontoUniversity of Waterloo
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhysical medicine and rehabilitationControl (management)Event (particle physics)NeurosciencePsychologyMedicineComputer scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Exposure to prisms has long been used to explore the control of visually guided actions primarily because adaptation requires the recalibration of misaligned reference frames due to perturbed visual input (i.e. eye-in-head and hand-centered reference frames must be realigned). To date, the only neuroimaging study to explore the direct effects of prisms on pointing used positron emission tomography and found increased activation only in right parietal cortex. We used event-related functional MRI to examine the effects of prisms on visuomanual pointing. Results demonstrated changes in activity in the anterior cingulate, the anterior intraparietal region and in a medial region of the right cerebellum. Specifically, activity in these regions was higher for the first few pointing trials made while viewing targets through prisms when directly contrasted to the last few trials. These results highlight that a more extensive network of cortical and cerebellar regions is involved in recalibrating visuomotor commands in the face of perturbed visual input.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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

Citations130
Published2008
Admission routes2
Has abstractyes

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Same venueEuropean Journal of NeuroscienceSame topicSpatial Neglect and Hemispheric DysfunctionFrench-language works237,207