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Record W4303473185 · doi:10.1101/2022.10.04.510901

Cortical Contributions to Medial Frontal β-Bursts during Executive Control

2022· preprint· en· W4303473185 on OpenAlexafffund
Steven P. Errington, Jacob A. Westerberg, Geoffrey F. Woodman, Jeffrey D. Schall

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsNeuroscienceElectroencephalographyMacaqueFrontal lobeCortex (anatomy)PsychologyCerebral cortexLocal field potentialSupplementary eye field

Abstract

fetched live from OpenAlex

ABSTRACT EEG β-bursts observed over the medial frontal cortex are claimed to mediate response inhibition despite their infrequent occurrence. The weak association with stopping behavior is supposed to be a by-product of the low signal-to-noise ratio of EEG recordings. We tested the premise that β-bursts are more common within the cerebral cortex and more directly associated with response inhibition. We sampled simultaneously EEG and intracortical local field potentials (LFP) within the medial frontal cortex (MFC) of two macaque monkeys performing a response inhibition task. Intracortical β-bursts were just as infrequent as those in EEG and did not parallel the likelihood of canceling a planned response. Cortical β-bursts were more prevalent in upper layers but were not synchronized across a cortical column or with EEG β-bursts. These findings contradict claims for a causal contribution of β-bursts during response inhibition, provide important constraints for biophysical and cortical circuit models, and invite further considerations of β-burst function in cognitive control.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.238
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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