A Comparison of Methods for Measuring Interocular Delays
Bibliographic record
Abstract
Many everyday tasks rely on binocular vision, which is impaired in individuals with amblyopia. Impairments in visual-spatial processing normally characterize amblyopia, but previous work has shown deficits in temporal processing as well, including processing delays in the amblyopic eye. Many techniques have been developed to measure interocular timing delays behaviourally by showing different images to the two eyes and recording participant responses. However, agreement between these measures has not been previously investigated. We compared four different assessment measures in normally-sighted observers: depth-based judgments (using the Pulfrich effect), interocular flicker integration, reaction time to monocular targets, and interocular temporal order judgments. Stimuli were presented using a high-speed projector with passive polarized filters (240 Hz per eye), enabling precise temporal control for dichoptic presentation. We also included a measure of sensory eye dominance to determine how eye dominance is related to each of the timing-based measurements. Pairwise comparisons of temporal delays measured across methods showed that the best-correlated pair of measures was between interocular flicker integration and temporal order judgements (r = 0.50). For each measure, we additionally calculated the average correlation between it and the remaining three measures. The Pulfrich effect was the best-correlated measure for examining timing delays between the eyes (Fisher Z = 0.24). In contrast, the measure that was least correlated with the other three measures was reaction time (Fisher Z = 0.09). Eye dominance was not correlated with the four temporal delay measures (Fisher Z = -0.01). Together, these results suggest that methods that rely on binocular integration are more reliable than monocular measurements. This highlights the importance of selecting appropriate tools for measuring interocular delays, and that suggests that combining specific methods may better characterize temporal delays seen in visual impairments.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".