Discriminating the direction of randomly positioned contrast-defined motion
Bibliographic record
Abstract
We investigated whether positional uncertainty affected observe' ability to discriminate the direction of luminance-defined and contrast-defined motion. If the mechanisms that detect contrast-defined motion can't be simultaneously monitored, not knowing the position of contrast-defined motion will severely affect performance. Random dot kinematograms were presented on a circular field (radius10 deg) of low contrast 2D binary noise. Dots were either brighter (luminance-defined) or higher contrast (contrast-defined) than the noise and moved at 3 deg/sec. In a circular target area (radius 1deg) the dots moved either up or down. The remaining dots, surrounding the target area, moved randomly. The target area was centered 2 deg from fixation and there were no dot-density cues to its location. Observers discriminated the direction of motion in the target area (2AFC method) when they knew its position and when it was randomly in 1 of 4 positions. Experiment 1 measured the modulation depth (contrast-defined patterns) or contrast (luminance-defined patterns) required to discriminate motion direction. Experiment 2 measured the number of coherently moving dots required to perform the same task. Both experiments were carried out with stimulus durations of 250ms and 100ms Thresholds for the motion in randomly positioned areas ranged from 1.1 to 3.4 times the thresholds for the motion in the known position. In experiment 1 the increase in threshold was slightly larger at the shorter duration. For each condition and observer the size of the effect was almost identical for luminance-defined and contrast-defined motion. Mechanisms for contrast-defined motion are not differentially affected in their ability to process motion signals of uncertain position compared with those for luminance-defined motion. Previous findings showing poor performance with multiple patches of contrast-defined motion must reflect some other deficiency in the mechanisms for contrast-defined motion.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 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".