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Record W2116794299 · doi:10.1037/a0033477

Power changes how the brain responds to others.

2013· article· en· W2116794299 on OpenAlexafffund
Jeremy Hogeveen, Michael Inzlicht, Sukhvinder S. Obhi

Bibliographic record

VenueJournal of Experimental Psychology General · 2013
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsThe Scarborough HospitalUniversity of TorontoWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMirroringPsychologyFunctional magnetic resonance imagingNeurosciencePower (physics)Mirror neuronPriming (agriculture)Human brainTranscranial magnetic stimulationAction (physics)Motor cortexCognitive psychologySocial psychologyStimulation

Abstract

fetched live from OpenAlex

Power dynamics are a ubiquitous feature of human social life, yet little is known about how power is implemented in the brain. Motor resonance is the activation of similar brain networks when acting and when watching someone else act, and is thought to be implemented, in part, by the human mirror system. We investigated the effects of power on motor resonance during an action observation task. Separate groups of participants underwent a high-, neutral, or low-power induction priming procedure, prior to observing the actions of another person. During observation, motor resonance was determined with transcranial magnetic stimulation (TMS) via measures of motor cortical output. High-power participants demonstrated lower levels of resonance than low-power participants, suggesting reduced mirroring of other people in those with power. These differences suggest that decreased motor resonance to others' actions might be one of the neural mechanisms underlying power-induced asymmetries in processing our social interaction partners.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.382
Teacher spread0.330 · 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 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

Citations132
Published2013
Admission routes2
Has abstractyes

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