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Record W2008004145 · doi:10.1152/jn.00995.2001

Modest Gaze-Related Discharge Modulation in Monkey Dorsal Premotor Cortex During a Reaching Task Performed With Free Fixation

2002· article· en· W2008004145 on OpenAlexaff
Paul Cisek, John Kalaska

Bibliographic record

VenueJournal of Neurophysiology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGazeNeuroscienceFixation (population genetics)PsychologyDorsumPremotor cortexEye movementElectrophysiologyAnatomyBiologyMedicinePopulation

Abstract

fetched live from OpenAlex

Recent studies have shown that gaze angle modulates reach-related neural activity in many cortical areas, including the dorsal premotor cortex (PMd), when gaze direction is experimentally controlled by lengthy periods of imposed fixation. We looked for gaze-related modulation in PMd during the brief fixations that occur when a monkey is allowed to look around freely without experimentally imposed gaze control while performing a center-out delayed arm-reaching task. During the course of the instructed-delay period, we found significant effects of gaze angle in 27-51% of PMd cells. However, for 90-95% of cells, these effects accounted for <20% of the observed discharge variance. The effect of gaze was significantly weaker than the effect of reach-related variables. In particular, cell activity during the delay period was more strongly related to the intended movement expressed in arm-related coordinates than in gaze-related coordinates. Under the same experimental conditions, many cells in medial parietal cortex exhibited much stronger gaze-related modulation and expressed intended movement in gaze-related coordinates. In summary, gaze direction-related modulation of cell activity is indeed expressed in PMd during the brief fixations that occur in natural oculomotor behavior, but its overall effect on cell activity is modest.

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 score0.963
Threshold uncertainty score0.469

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 communication0.0000.001
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 head0.019
GPT teacher head0.218
Teacher spread0.198 · 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

Citations79
Published2002
Admission routes1
Has abstractyes

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