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Strengthened Effective Connectivity Underlies Transfer of Working Memory Training to Tests of Short-Term Memory and Attention

2013· article· en· W2076323188 on OpenAlexaff
Bornali Kundu, David Sutterer, Stephen M. Emrich, Bradley R. Postle

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBrock University
FundersNational Institute of General Medical SciencesNational Institute of Mental Health
KeywordsWorking memoryCognitive psychologyStimulus (psychology)CognitionPsychologyElectroencephalographyTranscranial magnetic stimulationWorking memory trainingNeuroscienceTask (project management)Stimulation

Abstract

fetched live from OpenAlex

Although long considered a natively endowed and fixed trait, working memory (WM) ability has recently been shown to improve with intensive training. What remains controversial and poorly understood, however, are the neural bases of these training effects and the extent to which WM training gains transfer to other cognitive tasks. Here we present evidence from human electrophysiology (EEG) and simultaneous transcranial magnetic stimulation and EEG that the transfer of WM training to other cognitive tasks is supported by changes in task-related effective connectivity in frontoparietal and parieto-occipital networks that are engaged by both the trained and transfer tasks. One consequence of this effect is greater efficiency of stimulus processing, as evidenced by changes in EEG indices of individual differences in short-term memory capacity and in visual search performance. Transfer to search-related activity provides evidence that something more fundamental than task-specific strategy or stimulus-specific representations has been learned. Furthermore, these patterns of training and transfer highlight the role of common neural systems in determining individual differences in aspects of visuospatial cognition.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.457

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 integrity0.0000.000
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 head0.181
GPT teacher head0.359
Teacher spread0.179 · 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

Citations165
Published2013
Admission routes1
Has abstractyes

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