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Record W2056547755 · doi:10.1167/3.9.125

Eye-centered remapping of remembered visual space in human parietal cortex

2010· article· en· W2056547755 on OpenAlexaff
W. Pieter Medendorp, Herbert C. Goltz, Tutis Vilis, J. Douglas Crawford

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsWestern UniversityYork University
Fundersnot available
KeywordsPosterior parietal cortexFixation (population genetics)GazeEye movementPsychologyNeuroscienceParietal lobeComputer visionCognitive psychologyComputer scienceMedicinePopulation

Abstract

fetched live from OpenAlex

Neurophysiological evidence suggests that the monkey posterior parietal cortex represents and updates visual space in eye-centered coordinates (Duhamel et al. Science 1992; Batista et al. Science 1999). Using event-related fMRI, we investigated whether the human posterior parietal cortex employs a similar mechanism to internally update spatial representations for both eye and arm movements. We first identified a bilateral parietal region with activity related to the remembered location of a visual goal in contralateral space. We then tracked this activity while subjects viewed two sequentially flashed “goal” and “fixation” targets at randomized horizontal locations, shifted their eyes toward the fixation target, and then either looked or pointed toward the remembered location of the goal. On any given trial the goal target could be to the left or right of the fixation target. The fMRI signal was always related to the horizontal location of the remembered goal relative to gaze. When eye movements reversed the remembered horizontal goal location relative to the gaze fixation point, a dynamic shift in cortical activity from one hemisphere to the other occurred. We conclude that in the human posterior parietal cortex, spatial goals for eye and arm movements are stored and remapped in eye-centered coordinates.

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 score0.788
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.328
Teacher spread0.302 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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