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BIOFEEDBACK IN THE MAGNETIC FIELD (ON STEREOTOPOGRAPHY OF COGNITIVE FUNCTIONS)

2013· article· en· W2470431808 on OpenAlexaff
К. Г. Мажирина, M. A. Pokrovskiy, Mariya Rezakova, А. А. Савелов, Arseny A. Sokolov, Ye. D. Petrovskiy, Ye. A. Levshakova, Yu. A. Ganenko, М. Б. Штарк

Bibliographic record

VenueBulletin of Siberian Medicine · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsInternational Laboratory for Brain, Music and Sound Research
Fundersnot available
KeywordsImitationBiofeedbackVoxelPsychologyCognitionComputer scienceCognitive psychologyNeural correlates of consciousnessCognitive scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

The brain was mapped on-line using fMRI technology in the process of the development of self-regulation skills. We studied the dynamics of new neural networks being created in the real and simulated biofeedback models. It was shown that immersion in a virtual story brings about the large involvement of cortical areas, which are characterized by high values of voxels in the middle-temporal, occipital and frontal regions. We discuss the qualitative characteristics of the real and the imitation game periods.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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 designNot applicable
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

Citations2
Published2013
Admission routes1
Has abstractyes

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