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Theta phase resetting and the error‐related negativity

2006· article· en· W2134160462 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.

Bibliographic record

VenuePsychophysiology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Victoria
FundersNational Institute of Mental Health
KeywordsElectroencephalographyNegativity effectOscillation (cell signaling)Error-related negativityPsychologySet (abstract data type)Phase synchronizationSynchronization (alternating current)Noise (video)Phase (matter)Pattern recognition (psychology)Speech recognitionCognitive psychologyArtificial intelligenceComputer scienceNeuroscienceCognitionPhysicsAnterior cingulate cortex

Abstract

fetched live from OpenAlex

It has been proposed that the error-related negativity (ERN) is generated by phase resetting of theta-band EEG oscillations. The present research evaluates a set of analysis methods that have recently been used to provide evidence for this hypothesis. To evaluate these methods, we apply each of them to two simulated data sets: one set that includes theta phase resetting and a second that comprises phasic peaks embedded in EEG noise. The results indicate that the analysis methods do not effectively distinguish between the two simulated data sets. In particular, the simulated data set constructed from phasic peaks, though containing no synchronization of ongoing EEG activity, demonstrates properties previously interpreted as supporting the synchronized oscillation account of the ERN. These findings suggest that the proposed analysis methods cannot provide unambiguous evidence that the ERN is generated by phase resetting of ongoing oscillations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.230

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)0.0000.000

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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