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Record W4390110875 · doi:10.31234/osf.io/23pvw

Error-related electromyographic activity over the corrugator supercilii is associated with neural performance monitoring

2023· preprint· en· W4390110875 on OpenAlexaff
Nathaniel Elkins‐Brown, Blair Saunders, Michael Inzlicht

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFacial electromyographyPsychologyElectromyographyAffect (linguistics)Facial musclesFacial expressionAudiologyCognitive psychologyCognitionElectroencephalographyNeural activityCommunicationNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Emerging research in social and affective neuroscience has implicated a role for affect and motivation in performance-monitoring and cognitive control. No study, however, has investigated whether facial electromyography (EMG) over the corrugator supercilii—a measure associated with negative affect and the exertion of effort—is related to neural performance monitoring. Here, we explored these potential relationships by simultaneously measuring the error-related negativity (ERN), error positivity (Pe), and facial EMG over the corrugator supercilii muscle during a punished, inhibitory control task. We found evidence for increased facial EMG activity over the corrugator immediately following error responses, and this activity was related to the Pe for both between- and within-subject analyses. These results are consistent with the idea that early, avoidance-motivated processes are associated with performance monitoring, and that such processes may also be related to orienting towards errors, the emergence of error awareness, or both.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.173
GPT teacher head0.358
Teacher spread0.185 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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