Asymmetries between convergence and divergence reveal tonic vergence is dependent upon phasic vergence function
Bibliographic record
Abstract
Horizontal vergence eye movements are controlled by two processes, phasic and slow-tonic. Slow-tonic responses are hypothesized to be stimulated by the faster, pulse-step neural output of the phasic system. This suggests that the general behavior of each system should be similar; however, this relationship has yet to be investigated directly. We characterize the relationship between phasic and tonic vergence by quantifying directional asymmetries in the response properties of each mechanism to the same disparity amplitudes. Four subjects viewed symmetric steps in disparity dichoptically at 40 cm while eye movements were recorded with infrared oculography. First- and second-order phasic and slow-tonic convergence response properties increased linearly with disparity demand (p < 0.01), whereas divergence responses did not (p > 0.05). Phasic divergence responses were slower than convergence (p = 0.012) and were associated with a higher frequency of saccades (p < 0.001). The average rate of slow-tonic change was correlated to the average peak velocity of phasic vergence at the same vergence demand in both directions, r = 0.78, p < 0.0001. Clear directional asymmetries were observed in phasic and tonic vergence responses. The response properties of the slow-tonic mechanism varied directly with the peak velocity of the complementary phasic system. These results provide empirical evidence of the relationship between phasic and slow-tonic vergence, suggesting that the latter depends on the motor function of the former, specifically the peak velocity. The recruitment of additional oculomotor mechanisms, such as saccades, improved the phasic response properties of the slower divergence mechanism but did not directly influence the response behavior of the slow-tonic mechanism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".