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Record W2756242617 · doi:10.1371/journal.pbio.2002794

What do we talk about when we talk about rhythm?

2017· letter· en· W2756242617 on OpenAlexaff
Jonas Obleser, Molly J. Henry, Péter Lakatos

Bibliographic record

VenuePLoS Biology · 2017
Typeletter
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsBiologyRhythmEvolutionary biologyInternal medicine

Abstract

fetched live from OpenAlex

Neural oscillations align to external stimulus rhythms, such as the recurring onsets in rhythmic sequences, via neural entrainment-that is, adjustment to the oscillation's phase and period. As such, measures of neural phase coherence or "phase concentration" can inform us about the precision of entrainment. Entrainment of ongoing neural oscillations is considered a potent mechanism that the brain utilizes to generate temporal predictions and to aid active perception, at least in sensory cortices. However, as attempts to falsify or question this hypothesis are scarce, the idea that temporal predictions are instantiated in neural oscillatory entrainment should not be set in stone just yet.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.068
GPT teacher head0.302
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations67
Published2017
Admission routes1
Has abstractyes

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