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Record W2593916611 · doi:10.1155/2017/4780876

Aerobic Exercise Effects on Ocular Dominance Plasticity with a Phase Combination Task in Human Adults

2017· article· en· W2593916611 on OpenAlexafffund
Jiawei Zhou, Alexandre Reynaud, Robert F. Hess

Bibliographic record

VenueNeural Plasticity · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaWenzhou Medical University
KeywordsBinocular rivalryOcular dominanceMonocularPerceptBinocular visionPsychologyNeuroscienceVisual cortexVisual perceptionComputer scienceArtificial intelligencePerception

Abstract

fetched live from OpenAlex

Several studies have shown that short-term monocular patching can induce ocular dominance plasticity in normal adults, in which the patched eye becomes stronger in binocular viewing. There is a recent study showing that exercise enhances this plasticity effect when assessed with binocular rivalry. We address one question, is this enhancement from exercise a general effect such that it is seen for measures of binocular processing other than that revealed using binocular rivalry? Using a binocular phase combination task in which we directly measure each eye's contribution to the binocularly fused percept, we show no additional effect of exercise after short-term monocular occlusion and argue that the enhancement of ocular dominance plasticity from exercise could not be demonstrated with our approach.

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)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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