Direction discrimination thresholds in binocular, monocular, and dichoptic viewing: Motion opponency and contrast gain control
Bibliographic record
Abstract
We studied the binocular organization of motion opponency and its relationship to contrast gain control. Luminance contrast thresholds for discriminating direction of motion were measured for drifting Gabor patterns (target) presented on counterphase flickering Gabor patterns (pedestal). There were four presentation conditions: binocular, monocular, dichoptic, and half-binocular. For the half-binocular presentation, the target was presented to one eye while pedestals were presented to both eyes. In addition, to test for motion opponency, we studied two increment and decrement conditions, in which the target increased contrast for one direction of movement but decreased it for the opposite moving component of the pedestal. Threshold versus pedestal contrast functions showed a dipper shape, and there was a strong interaction between pedestal contrast and test condition. Binocular thresholds were lower than monocular thresholds but only at low pedestal contrasts. Monocular and half-binocular thresholds were similar at low pedestal contrasts, but half-binocular thresholds became higher and closer to dichoptic thresholds as pedestal contrast increased. Adding the decremental target reduced thresholds by a factor of two or more-a strong sign of opponency-when the decrement was in the same eye as the increment or the opposite eye. We compared several computational models fitted to the data. Converging evidence from the present and previous studies (Gorea, Conway, & Blake, 2001) suggests that motion opponency is most likely to be monocular, occurring before direction-specific binocular summation and before divisive, binocular gain control.
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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) | 0.001 | 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".