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Record W2772856686 · doi:10.1093/brain/awx330

The climate of neurofeedback: scientific rigour and the perils of ideology

2017· letter· en· W2772856686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBrain · 2017
Typeletter
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsJewish General HospitalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFundação Bial
KeywordsRigourNeurofeedbackIdeologyPsychologyEpistemologyNeuroscienceElectroencephalographyPolitical sciencePhilosophyPoliticsLaw

Abstract

fetched live from OpenAlex

Over the last six decades, an in-group with ideological and financial stakes has been conducting sub-par research to develop an ostensibly effective clinical intervention: EEG-neurofeedback. More recently, however, a string of independent studies featuring increased scientific rigour and tighter experimental controls has challenged the foundation on which EEG-neurofeedback stands. Earlier this year, Brain published one of the most robust EEG-neurofeedback experiments to date (Schabus et al., 2017), which sparked a flurry of correspondence concerning the therapeutic value of neurofeedback However, to effectively interpret the pro and con viewpoints, one must appreciate the peculiar culture surrounding the field of EEG-neurofeedback. The present breezy piece provides little-discussed yet highly relevant contextual information often absent from formal papers and technical reports.

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.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.390
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.007
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.293
Teacher spread0.257 · 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