MétaCan
Menu
Back to cohort
Record W2782393876 · doi:10.1111/cdev.13080

<i>N</i> -back Working Memory Task: Meta-analysis of Normative fMRI Studies With Children

2018· review· en· W2782393876 on OpenAlexafffund
Zachary Yaple, Marie Arsalidou

Bibliographic record

VenueChild Development · 2018
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaRussian Science Foundation
KeywordsWorking memoryn-backPsychologyFunctional magnetic resonance imagingNeuroimagingCognitive psychologyNormativeInsulaTask (project management)CognitionFunctional neuroimagingDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract The n-back task is likely the most popular measure of working memory for functional magnetic resonance imaging (fMRI) studies. Despite accumulating neuroimaging studies with the n-back task and children, its neural representation is still unclear. fMRI studies that used the n-back were compiled, and data from children up to 15 years (n = 260) were analyzed using activation likelihood estimation. Results show concordance in frontoparietal regions recognized for their role in working memory as well as regions not typically highlighted as part of the working memory network, such as the insula. Findings are discussed in terms of developmental methodology and potential contribution to developmental theories of cognition. The title for this Special Section is Meta-analysis and Individual Participant Data Synthesis in Child Development, edited by Glenn I. Roisman and Marinus H. van IJzendoorn

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)0.014
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.220
GPT teacher head0.361
Teacher spread0.141 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations103
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueChild DevelopmentSame topicNeuroscience and Music PerceptionFrench-language works237,207