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Record W2083721127 · doi:10.1167/13.5.12

Short-term monocular deprivation strengthens the patched eye's contribution to binocular combination

2013· article· en· W2083721127 on OpenAlexafffund
Jiawei Zhou, Simon Clavagnier, R. F. Hess

Bibliographic record

VenueJournal of Vision · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsOcular dominanceMonocularMonocular deprivationBinocular visionSensory systemSensory deprivationDominance (genetics)PerceptionPopulationMonocular visionPsychologyOptometryNeuroscienceVisual cortexBiologyComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Binocularity is a fundamental property of primate vision. Ocular dominance describes the perceptual weight given to the inputs from the two eyes in their binocular combination. There is a distribution of sensory dominance within the normal binocular population with most subjects having balanced inputs while some are dominated by the left eye and some by the right eye. Using short-term monocular deprivation, the sensory dominance can be modulated as, under these conditions, the patched eye's contribution is strengthened. We address two questions: Is this strengthening a general effect such that it is seen for different types of sensory processing? And is the strengthening specific to pattern deprivation, or does it also occur for light deprivation? Our results show that the strengthening effect is a general finding involving a number of sensory functions, and it occurs as a result of both pattern and light deprivation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.332
Teacher spread0.300 · 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 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

Citations139
Published2013
Admission routes2
Has abstractyes

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