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Record W2036936665 · doi:10.1177/1550059414563746

Enhancing Cognitive Function Using Perceptual-Cognitive Training

2014· article· en· W2036936665 on OpenAlexaff
Brendan Parsons, Tara Magill, Alexandra Boucher, Monica Zhang, Katrine Zogbo, Sarah Bérubé, Olivier Scheffer, Mario Beauregard, Jocelyn Faubert

Bibliographic record

VenueClinical EEG and Neuroscience · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsConcordia UniversityUniversité de MontréalMcGill University
Fundersnot available
KeywordsCognitionCognitive trainingElectroencephalographyNeuropsychologyWorking memoryPsychologyPerceptionCognitive psychologyVisual perceptionAudiologyPopulationNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Three-dimensional multiple object tracking (3D-MOT) is a perceptual-cognitive training system based on a 3D virtual environment. This is the first study to examine the effects of 3D-MOT training on attention, working memory, and visual information processing speed as well as using functional brain imaging on a normative population. Twenty university-aged students were recruited and divided into a training (NT) and nonactive control (CON) group. Cognitive functions were assessed using neuropsychological tests, and correlates of brain functions were assessed using quantitative electroencephalography (qEEG). Results indicate that 10 sessions of 3D-MOT training can enhance attention, visual information processing speed, and working memory, and also leads to quantifiable changes in resting-state neuroelectric brain function.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.832
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.443
GPT teacher head0.476
Teacher spread0.033 · 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

Citations110
Published2014
Admission routes1
Has abstractyes

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