Integration of Intermittent Visual Samples Over Time and Between the Eyes
Bibliographic record
Abstract
The authors investigated the integration of alternate disparate monocular inputs for binocular perception in 1-handed catching experiments (N = 14, 32, 22, and 15 participants, respectively in Experiments 1-4). They varied the no-vision interval between alternate monocular samples to measure catching performance, and they compared the alternating monocular conditions with binocular and monocular conditions with equal no-vision intervals. They found no evidence of a binocular advantage for one-handed catching in the alternating monocular conditions. Performance in monocular and alternating monocular conditions did not differ across no-vision intervals ranging from 0-80 ms and was particularly worse than performance in binocular viewing conditions when the no-vision interval was 40 ms or more. The authors argue that the dissimilarity between disparate monocular inputs created by the approaching object limited the integration of those inputs and subsequent binocular perception.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".