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Working Memory Training in Healthy Adults

2016· article· en· W2308094331 on OpenAlexaboutno aff
Linette Lawlor‐Savage

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Working memory trainingCognitive psychologyPsychologyComputer scienceWorking memoryCognitionNeuroscienceGeography

Abstract

fetched live from OpenAlex

This is a database of healthy adults, aged 30-60, who participated in a study comparing online working memory training to online processing speed training. Participants are English speaking and data was collected at the University of Calgary, Calgary, Alberta, Canada between 2011-2013.Participants completed a demographics questionnaire, a brief measure of intelligence (Wechsler Abbreviated Scale of Intelligence; WASI), and measures of working memory, processing speed, and fluid intelligence. After completing 5-weeks of working memory training or processing speed training, participants again completed measures of working memory, processing speed, and fluid intelligence. Of the 81 participants who consented to participate, 61 (75%) completed baseline and post-training testing. Four participants were removed from analysis due to low training dosage, resulting in 57 participants.The University of Calgary Conjoint Faculties Research Ethics Board approved this study, and all participants provided written informed consent.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.154
GPT teacher head0.342
Teacher spread0.188 · 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 designNon-randomized trial
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

Citations0
Published2016
Admission routes1
Has abstractyes

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