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Record W2135770920 · doi:10.1037/a0022216

Working memory capacity and go/no-go task performance: Selective effects of updating, maintenance, and inhibition.

2011· article· en· W2135770920 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsYork University
Fundersnot available
KeywordsTask (project management)Working memoryCognitive psychologyPsychologyGo/no goLagInterference theoryComputer scienceCognitionNeuroscienceMachine learning

Abstract

fetched live from OpenAlex

The ability to temporarily maintain information in order to successfully perform a task is important in many daily activities. However, the ability to quickly and accurately update existing mental representations in distracting situations is also imperative in many of these same circumstances. In the current studies, individuals varying in working memory capacity (WMC) performed different varieties of go/no-go tasks that have been hypothesized to measure inhibitory ability. The results indicated that low-WMC individuals relative to high-WMC individuals showed worse performance specifically in certain conditions of the conditional go/no-go task. Further analyses showed that increasing the temporal lag/number of intervening items between the previous target and the current lure had a deleterious effect on the performance of the low-WMC group only. The results indicate a relationship between WMC and the ability to selectively update, maintain, and retrieve information, especially in interference-rich conditions.

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.

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)0.000
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 score0.036
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

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

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 head0.073
GPT teacher head0.322
Teacher spread0.249 · 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