MétaCan
Menu
Back to cohort

Visuospatial updating of reaching targets in near and far space

2002· article· en· W2397463131 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeuroreport · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsYork UniversityCanadian Institutes of Health Research
Fundersnot available
KeywordsEye movementPosterior parietal cortexNeuroscienceMechanism (biology)Contrast (vision)PsychologySpace (punctuation)Visual spaceComputer scienceArtificial intelligenceComputer visionPerceptionPhysics

Abstract

fetched live from OpenAlex

The brain constructs multiple representations of near and far space but it is unclear which spatial mechanism guides reaching across eye movements in near space. Retinocentric reaching representations are known to exist in parietal cortex, but must be updated during eye movements, in order to remain accurate. In contrast, non-retinal (e.g. muscle-centered) reaching plans in motor cortex do not require updating, and so may provide a more stable encoding mechanism. To test between these, we employed a behavioral test. Subjects briefly foveated a target (located at various depths in near and far space) looked peripherally, then reached toward its remembered location. Surprisingly, subjects did not use the stable non-retinal reaching plan (compared to controls without eye movements). Instead, the intervening eye movements induced a systematic pattern of reaching errors for targets at all depths consistent with updating in a retinal frame. We conclude that a common eye-centered updating mechanism prevails in programming arm movements in both near and far space.

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.

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)0.001
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 score0.815
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.249
Teacher spread0.216 · 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