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Record W4225086338 · doi:10.3389/feduc.2021.812760

Cognitive and Linguistic Effects of Working Memory Training in Children With Corresponding Deficits

2022· article· en· W4225086338 on OpenAlexaff
Laura J. Pauls, Lisa M. D. Archibald

Bibliographic record

VenueFrontiers in Education · 2022
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsWestern University
Fundersnot available
KeywordsWorking memory trainingWorking memoryPsychologyCognitive psychologyReading (process)Transfer of trainingCognitionCognitive trainingDevelopmental psychologyIntervention (counseling)Transfer of learningMultiple baseline designShort-term memoryLinguistics

Abstract

fetched live from OpenAlex

Working memory training has been found to result in improvements on tasks similar to those involved in the training (near transfer) but show limited impact on everyday skills such as language or academic abilities (far transfer). Previous research has largely focused on those with a broad range of skills, and examined group level responses. This study uses a single subject design to examine the effect of working memory training on both working memory and related domains in children with working memory impairment. Seven children (8–11 years old) with a working memory impairment completed 20 sessions of computerized working memory intervention. Data revealed near transfer for all participants. Evidence of far transfer to improvements in language, reading, or math was observed for approximately half of the participants on individual measures. Three participants showed convincing but modest training effects across multiple measures. A combination of factors appeared to influence far transfer including age, training intensity, and baseline measures.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.282
Teacher spread0.263 · 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 designObservational
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

Citations10
Published2022
Admission routes1
Has abstractyes

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