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Record W2028746805 · doi:10.1155/2014/961781

Letter and Colour Matching Tasks: Parametric Measures of Developmental Working Memory Capacity

2014· article· en· W2028746805 on OpenAlexafffund
Tamara L. Powell, Marie Arsalidou, Vanessa M. Vogan, Margot J. Taylor

Bibliographic record

VenueChild Development Research · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCanada Research ChairsSickKids FoundationHospital for Sick ChildrenYork UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyWorking memoryTask (project management)RecallCognitionCognitive psychologyMatching (statistics)Interference theoryInterference (communication)Developmental psychologyMemory developmentCognitive developmentComputer scienceNeuroscienceStatisticsMathematics

Abstract

fetched live from OpenAlex

We investigated the mediating role of interference in developmental assessments of working memory (WM) capacity across childhood, adolescence, and young adulthood. One hundred and forty-two participants completed two versions of visuospatial (colour matching task, CMT) and verbal (letter matching task, LMT) WM tasks, which systematically varied cognitive load in a high and low interference condition. Results showed similar developmental trajectories across high interference contexts (CMT- and LMT-Complex) and divergent developmental growth patterns across low interference contexts (CMT- and LMT-Simple). Performance on tasks requiring greater cognitive control was in closer agreement with developmental predictions relative to simple recall guided tasks that rely solely on the storage components of WM. These findings suggest that developmental WM capacity, as measured by the CMT and LMT paradigms, can be better quantified using high interference contexts, in both content domains, and demonstrate steady increases in WM through to mid-adolescence.

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.381
GPT teacher head0.399
Teacher spread0.018 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations12
Published2014
Admission routes2
Has abstractyes

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