Binocular Balance in Normal Vision and Its Modulation by Mean Luminance
Bibliographic record
Abstract
PURPOSE: To better understand the neural basis of sensory dominance in the normal population, we applied a recently established test designed to quantify the degree of suppression in amblyopia to participants with normal binocular vision. METHODS: This test quantifies the degree of dichoptic imbalance in coherent motion sensitivity by manipulating the contrast of stimuli seen by the two eyes. The contrast at which balanced dichoptic motion sensitivity occurs is referred to as the "balance point" and is an estimate of the degree of suppression. We apply the same logic to the measurement of sensory dominance by measuring the distribution of "balance points" within the normal population. RESULTS: We show that although most subjects are balanced or only weakly imbalanced, a minority is strongly imbalanced. To ascertain the site of sensory dominance, we assessed the degree to which normal sensory balance can be modulated by changing the interocular mean luminance. We found that mismatches in mean luminance between the two eyes had a pronounced effect on the balance point determination. CONCLUSIONS: Because cells in the lateral geniculate nucleus exhibit a strong modulation to sustained changes in the mean light level, this may suggests that the inhibitory circuits underlying sensory eye dominance are located at a precortical site.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".