Visual search deficits in amblyopia
Bibliographic record
Abstract
Amblyopia is a neurodevelopmental disorder defined as a reduction in visual acuity that cannot be corrected by optical means. It has been associated with low-level deficits. However, research has demonstrated a link between amblyopia and visual attention deficits in counting, tracking, and identifying objects. Visual search is a useful tool for assessing visual attention but has not been well studied in amblyopia. Here, we assessed the extent of visual search deficits in amblyopia using feature and conjunction search tasks. We compared the performance of participants with amblyopia (n = 10) to those of controls (n = 12) on both feature and conjunction search tasks using Gabor patch stimuli, varying spatial bandwidth and orientation. To account for the low-level deficits inherent in amblyopia, we measured individual contrast and crowding thresholds and monitored eye movements. The display elements were then presented at suprathreshold levels to ensure that visibility was equalized across groups. There was no performance difference between groups on feature search, indicating that our experimental design controlled successfully for low-level amblyopia deficits. In contrast, during conjunction search, median reaction times and reaction time slopes were significantly larger in participants with amblyopia compared with controls. Amblyopia differentially affects performance on conjunction visual search, a more difficult task that requires feature binding and possibly the involvement of higher-level attention processes. Deficits in visual search may affect day-to-day functioning in people with amblyopia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".