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Record W2168609040 · doi:10.1163/18784763-00002391

The Effect of Video Game Training on the Vision of Adults with Bilateral Deprivation Amblyopia

2012· article· en· W2168609040 on OpenAlexafffund
Seong Taek Jeon, Daphne Maurer, Terri L. Lewis

Bibliographic record

VenueSeeing and Perceiving · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsContrast (vision)Visual acuityPsychologyVideo gameSensory deprivationCataractsCrowdingAudiologyVisual fieldOphthalmologyMedicineSensory systemCognitive psychologyArtificial intelligenceNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Amblyopia is a condition involving reduced acuity caused by abnormal visual input during a critical period beginning shortly after birth. Amblyopia is typically considered to be irreversible during adulthood. Here we provide the first demonstration that video game training can improve at least some aspects of the vision of adults with bilateral deprivation amblyopia caused by a history of bilateral congenital cataracts. Specifically, after 40 h of training over one month with an action video game, most patients showed improvement in one or both eyes on a wide variety of tasks including acuity, spatial contrast sensitivity, and sensitivity to global motion. As well, there was evidence of improvement in at least some patients for temporal contrast sensitivity, single letter acuity, crowding, and feature spacing in faces, but not for useful field of view. The results indicate that, long after the end of the critical period for damage, there is enough residual plasticity in the adult visual system to effect improvements, even in cases of deep amblyopia caused by early bilateral 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 head0.029
GPT teacher head0.293
Teacher spread0.265 · 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 designNon-randomized trial
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

Citations53
Published2012
Admission routes2
Has abstractyes

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