On the Relationship Between Sensory Eye Dominance and Stereopsis in the Normal-Sighted Adult Population: Normative Data
Bibliographic record
Abstract
The extent of sensory eye dominance, a reflection of the interocular suppression in binocular visual processing, can be quantitatively measured using the binocular phase combination task. In this study, we aimed to provide a normative dataset for sensory eye dominance using this task. Based on that, we also assessed the relationship between perceptual eye dominance and stereopsis. 142 adults (average age: 24.00 ± 1.70 years old) with normal or corrected to normal monocular visual acuity (logMAR < 0.00) participated. Observer’s sensory eye dominance was quantified in two complementary ways: the interocular contrast ratio when the two eyes were balanced (i.e., the balance point) and the absolute value of the binocular perceived phase when each eye viewed maximum contrast stimuli in binocular phase combination task. Stereo acuities were measured with maximum contrast stimuli using an identical spatial frequency (0.30 cycles/degree) and stimulus arrangement to that used in the eye dominance assessment. The averaged balance point was 0.92 ± 0.06 (Mean ± SD), the averaged absolute value of the binocular perceived phase when both eyes viewed maximum contrast stimuli was 7.62 ± 6.91 degrees, and the averaged stereo acuity was 2.19 ± 0.34 log arc seconds. Neither of these two sensory eye dominance measures were significantly correlated with stereo acuity (Balance point: rho = 0.14, P = 0.10; Phase: rho = -0.13, P = 0.13). The sensory eye dominance, as reflected using a phase combination task, and stereopsis are not significantly correlated in the normal-sighted population at low spatial frequencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".