MétaCan
Menu
Back to cohort
Record W2083229040 · doi:10.4021/jocmr727w

The Effect of Motor Learning of Serial Reaction Time Task (SRTT) Through Action Observation on Mu Rhythm and Improvement of Behavior Abilities

2012· article· en· W2083229040 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Clinical Medicine Research · 2012
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsnot available
Fundersnot available
KeywordsSerial reaction timeMotor learningSignificant differenceStimulus (psychology)Task (project management)Action (physics)MedicineCognitive psychologyRhythmAudiologyPsychologyNeuroscienceSequence learningInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was investigate whether an action observation would have an effect on the action that requires task understanding in humans. METHODS: Participants who met the criteria for this study (n = 36). To evaluate the performance, reaction time and performance accuracy, the stimulus scheduling software was employed. For the electroencephalogram, the equipment QEEG-8 was used. RESULTS: Concerning the reaction time of the groups of different learning methods a significant difference was found after the learning among the three groups. Regarding the accuracy among the groups of different learning methods, significant difference was found in the action performance accuracy among the three groups. The relative mu power during the SRTT implementation was compared in the CZ, C3 and C4 regions before, during, and after the learning for each group. In the CZ and C4 region, a significant difference was found in the action observation group. In the C3 and C4 region a significant difference was found in the actual practice group. CONCLUSIONS: The result suggests that imitation and learning are involved even in the action that requires task understanding in humans. KEYWORDS: Accuracy; Mirror neuron system; Mu Rhythm; Reaction time; SRTT.

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)0.013
metaresearch head score (Gemma)0.008
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 score0.569
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.008
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 integrity0.0000.001
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 head0.253
GPT teacher head0.548
Teacher spread0.295 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Medicine ResearchSame topicAction Observation and SynchronizationFrench-language works237,207