MétaCan
Menu
Back to cohort
Record W2058954627 · doi:10.1152/jn.00330.2003

Contribution of Head Movement to Gaze Command Coding in Monkey Frontal Cortex and Superior Colliculus

2003· article· en· W2058954627 on OpenAlexaff
Julio Martínez-Trujillo, Eliana M. Klier, Hongying Wang, J. Douglas Crawford

Bibliographic record

VenueJournal of Neurophysiology · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsSuperior colliculusGazeSupplementary eye fieldNeurosciencePsychologyEye movementHead (geology)CommunicationSaccadic maskingBiology

Abstract

fetched live from OpenAlex

Most of what we know about the neural control of gaze comes from experiments in head-fixed animals, but several "head-free" studies have suggested that fixing the head dramatically alters the apparent gaze command. We directly investigated this issue by quantitatively comparing head-fixed and head-free gaze trajectories evoked by electrically stimulating 52 sites in the superior colliculus (SC) of two monkeys and 23 sites in the supplementary eye fields (SEF) of two other monkeys. We found that head movements made a significant contribution to gaze shifts evoked from both neural structures. In the majority of the stimulated sites, average gaze amplitude was significantly larger and individual gaze trajectories were significantly less convergent in space with the head free to move. Our results are consistent with the hypothesis that head-fixed stimulation only reveals the oculomotor component of the gaze shift, not the true, planned goal of the movement. One implication of this finding is that when comparing stimulation data against popular gaze control models, freeing the head shifts the apparent coding of gaze away from a "spatial code" toward a simpler visual model in the SC and toward an eye-centered or fixed-vector model representation in the SEF.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.317

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 head0.038
GPT teacher head0.314
Teacher spread0.276 · 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

Citations37
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurophysiologySame topicVisual perception and processing mechanismsFrench-language works237,207