Repulsion of perceived direction in superimposed surfaces
Bibliographic record
Abstract
Under the proper conditions, two moving gratings that are superimposed are perceived as a plaid moving in the vector average of the 2 grating directions. Much research has been done both psychophysically and through neuronal recordings to understand the process of integrating the two gratings into a single plaid object. A field of dots (random dot kinetograms) that moves coherently is perceived as a surface. When two dot fields are superimposed upon each other, moving in different directions, no such integration occurs. Instead, the dots are segmented into two distinct objects. We investigated whether the perceived directions of motion of two superimposed surfaces would still be affected by the process of direction integration. Subjects fixated a central cross while an aperture containing 2 surfaces moving in different directions appeared in the lower right or lower left visual fields. After 1000 ms, the surfaces and fixation cross were removed while a white circular outline of the aperture appeared. Subjects used a mouse to click on the perceived directions of motion for each of the 2 surfaces. We expected to find that the difference in the perceived directions would be less than the actual difference between the directions, as this would be consistent with (weak) integration. Surprisingly, we found the opposite effect. The difference in perceived directions was significantly larger than the difference in actual directions. These results suggest that unlike the integration of moving gratings into a plaid, superimposed surfaces comprised of random dot kinetograms are repulsed. The key factor is that the RDKs are automatically segmented into two objects providing a substrate for competitive interactions. Thus, the repulsion of perceived direction is likely due to competitive circuits previously identified for attentional modulation in area MT.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".