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Record W3217297165 · doi:10.1002/acp.3904

Lifestyle factors and their impact on the networks of attention

2021· article· en· W3217297165 on OpenAlexaff
Colin R. McCormick

Bibliographic record

VenueApplied Cognitive Psychology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychologyCognitionPremisePsychological interventionAttention networkCognitive psychologyMeditationExecutive functionsPsychiatry

Abstract

fetched live from OpenAlex

Abstract In 2002, Fan and his colleagues developed the Attention Network Test (ANT), a cognitive tool that provides a score for each of the attentional networks (alerting, orienting, and executive functioning). Since publication, this study has been cited over 3 500 times. The authors state one of the indicated uses of this tool is to measure how different interventions, both behavioral and pharmacological, influence the networks of attention. The present review focuses on this premise and investigates how various aspects of lifestyle differently impact the networks of attention. Whether trying to optimize the attentional networks to improve cognitive performance, or to prevent the cognitive decline that occurs with age, this review summarizes what practices promote efficiency within the alerting, orienting, and executive functioning networks. The specific areas of lifestyle this review focuses on are meditation, exercise, drug use, sleep, and environmental or social factors.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.381
Teacher spread0.271 · 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 designObservational
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

Citations13
Published2021
Admission routes1
Has abstractyes

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