Binocular contributions to motion detection and motion discrimination during locomotion
Bibliographic record
Abstract
During locomotion, the visual system can factor out the motion component caused by observer locomotion from the complex target flow vector to obtain the world-relative target motion. This process, which has been termed flow parsing, is known to be incomplete, but viewing with both eyes could potentially aid in this task. Binocular disparity and binocular summation could both improve performance when viewing with both eyes. To separate the binocular disparity and binocular summation and analyse how they affect flow parsing, we tested detection and discrimination thresholds under three viewing conditions: stereoscopic, synoptic (binocular but without disparity) and monocular. Experiment 1 tested motion detection during simulated forward self-motion and when stationary. Experiment 2 and 3 tested motion discrimination in forward and backward self-motion and stationary conditions. We found that binocular disparity significantly improved detection thresholds and discrimination biases, at the cost of lower precision. Binocular summation only significantly improved detection thresholds when stationary. It did not significantly affect detection thresholds during locomotion, discrimination biases, or discrimination precisions. Our results indicated that both binocular summation and binocular disparity contribute to motion detection and motion discrimination, but they affect performance differently while stationary and during locomotion.
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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 | 0.001 |
| 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".