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Record W4409838469 · doi:10.1016/j.visres.2025.108606

Attention deficits in Amblyopia: A narrative review

2025· review· en· W4409838469 on OpenAlexaff
Benjamin Thompson

Bibliographic record

VenueVision Research · 2025
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Waterloo
FundersInnovation and Technology Commission - Hong KongInnovation and Technology Commission
KeywordsPsychologyNarrativeOptometryCognitive psychologyMedicineLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Amblyopia has effects on vision that extend from the processing of low-level visual features to higher level functions such as visual attention. In this narrative review, we focus on the impact of amblyopia on visual attention. A structured literature search revealed 28 articles reporting comparisons between amblyopia and normal vision control groups for a variety of visual attention tasks. Several of these articles also included neuroimaging measures. A review of these articles suggested that amblyopia does not affect behavioral performance of tasks with a low attentional load, such as cuing tasks, but deficits emerge for tasks with high demands on visual attention such as multiple object tracking. Deficits are not limited to the amblyopic eye but are also evident under fellow eye and binocular viewing conditions suggesting that abnormal early binocular visual experience can fundamentally alter the development of visual attention. Overall, the current literature suggests that amblyopia is associated with reduced visual attention resources. We raise the possibility that this attention resource deficit may be partially associated with an attentional demand for suppression of the amblyopic eye.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.235
GPT teacher head0.608
Teacher spread0.374 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes1
Has abstractyes

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