Poor encoding of position by contrast-defined motion
Bibliographic record
Abstract
Second-order (contrast-defined) motion stimuli lead to poor performance on a number of tasks, including discriminating form from motion and visual search. To investigate this deficiency, we tested the ability of human observers to monitor multiple regions for motion, to code the relative positions of shapes defined by motion, and to simultaneously encode motion direction and location. Performance with shapes from contrast-defined motion was compared with that obtained from luminance-defined (first-order) stimuli. When the position of coherent motion was uncertain, direction-discrimination thresholds were elevated similarly for both luminance-defined and contrast-defined motion, compared to when the stimulus location was known. The motion of both luminance- and contrast-defined structure can be monitored in multiple visual field locations. Only under conditions that greatly advantaged contrast-defined motion, were observers able to discriminate the positional offset of shapes defined by either type of motion. When shapes from contrast-defined and luminance-defined motion were presented under comparable conditions, the positional accuracy of contrast-defined motion was found to be poorer than its luminance-defined counterpart. These results may explain some, but possibly not all, of the deficits found previously with second-order 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".