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Record W4412838354 · doi:10.1093/cercor/bhaf159

Two premotor areas (6aα and 6aγ) involved in motor preparation and execution during visually guided locomotion in the cat

2025· article· en· W4412838354 on OpenAlexafffund
Nicolas Fortier-Lebel, Toshi Nakajima, Trevor Drew

Bibliographic record

VenueCerebral Cortex · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsSupplementary motor areaPremotor cortexMotor areaPosterior parietal cortexCortex (anatomy)Motor cortexGaitNeurosciencePrimary motor cortexPsychologyComputer scienceAnatomyBiologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

We examined the contribution of areas 6aα and 6aγ of the cat pericruciate cortex to the control of visually guided locomotion by recording single unit activity from layer V of the cortex while cats stepped over obstacles attached to a moving belt. We found populations of neurons in both areas (53% in area 6aα; 25% in area 6aγ) that modified their discharge activity in advance of and/or during the steps over the obstacle. In both areas we found some cells that modified their activity only when the contralateral or ipsilateral limb was the first to step over the obstacle and others that discharged regardless of which limb was the first to step over. Retrograde tracer injections into each area showed differential projections from the primary motor cortex (area 4γ), with area 6aα receiving inputs from rostromedial 4γ (controlling more proximal movements) and 6aγ receiving inputs from more rostrolateral (controlling more distal movements) regions of 4γ. Area 6aα, in addition, received inputs from area 6iffu. Both regions received inputs from area 5 of the posterior parietal cortex. The results support a role for each area in the planning and execution of visually guided gait modifications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.412

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 head0.023
GPT teacher head0.292
Teacher spread0.269 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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