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Record W6925241721 · doi:10.17605/osf.io/8fwqu

Measuring brain activity when exposed to infant-directed speech: A meta-analysis

2023· other· en· W6925241721 on OpenAlexaff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicScience and Science Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBrain activity and meditationCognitionNeuroimagingMental activityFunctional near-infrared spectroscopyHuman brainNeural activityPremovement neuronal activityBrain mapping

Abstract

fetched live from OpenAlex

In recent years, the number of infant studies using functional Near-Infrared Spectroscopy (fNIRS) has been steadily increasing. One of the main focuses in developmental cognitive neuroscience research is uncovering the brain correlates of speech processing. This is a meta-analysis, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, investigating the brain regions activated when presented with speech during infancy. Specifically, we will report on the field-wide result of whether the frontal and temporal lobes of both or either hemispheres are active when neonate to 12-month-old infants perceive auditory or audiovisual stimuli in studies employing fNIRS methodologies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.033
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.140
GPT teacher head0.349
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainMethods
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

Citations0
Published2023
Admission routes1
Has abstractyes

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