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Record W4415949842 · doi:10.1016/j.cortex.2025.10.009

Motor preparation during pain observation does not influence event-related Mu and Beta desynchronization

2025· article· en· W4415949842 on OpenAlexafffund
Carl Michael Galang, Michael Jenkins, Taryn Sanders, R. K. Vijh, Sukhvinder S. Obhi

Bibliographic record

VenueCortex · 2025
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcMaster UniversityBrock University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsEmpathyElectroencephalographyFunctional magnetic resonance imagingBeta RhythmMirror neuronAction (physics)Motor areaBETA (programming language)

Abstract

fetched live from OpenAlex

Previous EEG research has shown that observing others in pain increases sensorimotor activity, as indexed by Mu (7-12 Hz) and Beta (13-30 Hz) desynchronization. Such activity is often interpreted as reflecting empathic processing through shared neural representations between the observer and target. In everyday life, observing another in pain can trigger a range of potential action tendencies (e.g., withdrawing, helping, or protecting oneself), but EEG studies typically restrict movement to avoid artifacts. This immobility may produce an artificial scenario that limits our understanding of how motor readiness and empathic processing interact. The present study examined whether engaging the motor system (via a simple key press) modulates these neural responses. Participants observed videos and pictures of a hand being stabbed by a needle or touched by a Q-tip. In half of the blocks, they prepared and executed a speeded key press to a Go signal; in the other half, they remained still. Results revealed Mu and Beta desynchronization during pain observation regardless of movement condition, replicating prior findings. These effects were unrelated to reaction times or empathy traits, suggesting that sensorimotor resonance during pain observation reflects a stable response rather than one contingent on task-specific motor preparation.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.579

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 head0.011
GPT teacher head0.292
Teacher spread0.280 · 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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