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Record W4306407854 · doi:10.1111/psyp.14194

Bilateral motor cortex functional differences in left‐handed approaching–avoiding behavior

2022· article· en· W4306407854 on OpenAlexaff
Xue Xia, Yan‐Ling Pi, Jing Xia, Yansong Li, Qingde Shi, Jian Zhang, Xiao-Ying Tan

Bibliographic record

VenuePsychophysiology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsTranscranial magnetic stimulationPsychologyStimulus (psychology)Motor cortexNeuroscienceNeurophysiologyPrimary motor cortexStimulationAudiologyMotor systemCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

Automatic action tendencies occur at behavioral and neurophysiological levels during task performance with the dominant right hand, with shorter reaction times (RTs) and higher excitability of the contralateral primary motor cortex (M1) during automatic vs. regulated behavior. However, effects associated with the non-dominant left-hand in approaching-avoiding behavior remain unclear. Here, we used transcranial magnetic stimulation during the performance by 18 participants of an approaching-avoiding task using the non-dominant left hand. Single-pulse transcranial magnetic stimulation was applied over left or right M1 at 150 and 300 ms after the onset of an emotional stimulus. RTs and motor-evoked potentials (MEPs) were recorded. Significant automatic action tendencies were observed at the behavioral level. Higher MEP amplitudes were detected 150 ms after stimulus onset from the right hand (non-task hand, corresponding to left M1) during regulated behavior compared with during automatic behavior. However, no significant modulation was found for MEP amplitudes from the left hand (task hand, corresponding to right M1). These findings suggested that left M1 may play a principal role in the early phase of mediating left-handed movement toward an emotional stimulus.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score1.000

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)0.0010.000

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 head0.075
GPT teacher head0.282
Teacher spread0.207 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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