Does the baseline motor response predict the short-term adaptability of phasic vergence?
Bibliographic record
Abstract
It has been hypothesized that faster, more accurate baseline neural-motor responses result in greater adaptability to repeated external perturbations. Like saccades, phasic convergence exhibits robust adaptive behavior when exposed to double-step gap stimuli. Directional asymmetries exist in the non-adapted baseline motor response of this phasic vergence mechanism to convergent or divergent disparities. We leverage these directional asymmetries to investigate the relationship between the baseline motor response and its adaptability to a double-step convergent or divergent stimuli. 10 adults (26±3.8y/o) completed 2 study visits where baseline convergence or divergence responses to a 2° disparity step were measured and then adapted using an increasing double-step stimuli (2°+1.5°, 175ms). Individual eye movements were recorded at 250Hz with infrared video oculography, while stimuli where presented dichoptically at 40cm. Vergence kinematics of baseline and adapted responses were compared between stimulus directions. Compared to convergence, divergence exhibited significantly less adaptive changes in gain (9±2%, vs. 31±3% p=0.0005), peak velocity (4±4% vs. 32±3% p=0.0001) and peak acceleration (3±5% vs.30±6%, p = 0.006). Only divergence gain was altered after adaptation (p = 0.005); while divergence peak velocity (p = 0.36) and peak acceleration (p=0.63) were unchanged. Adapted divergence response duration increased (25±9ms, p=0.03), whereas adapted convergence duration was unchanged (-6±9ms, p=0.97). Baseline convergence peak velocity was faster (12.5±1.4°/s vs. 8.7±2.4°/s, p=0.004) than divergence in all subjects. Baseline vergence peak velocity was the strongest predictor of the adaptability of the gain and peak velocity of each system. The results demonstrate that phasic convergence adapts to systematic errors by altering all orders of the dynamic response, whereas phasic divergence adapts by altering only the duration of response output. This adaptive behavior is most strongly correlated with the initial peak velocity of the response, suggesting the baseline neural-motor function determines the degree of adaptability within this oculomotor system. Meeting abstract presented at VSS 2017
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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) | 0.000 | 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".