Role of primary visual cortex in the binocular integration of plaid motion perception
Bibliographic record
Abstract
This study assessed the early mechanisms underlying perception of plaid motion. Thus, two superimposed gratings drifting in a rightward direction composed plaid stimuli whose global motion direction was perceived as the vector sum of the two components. The first experiment was aimed at comparing the perception of plaid motion when both components were presented to both eyes (dioptic) or separately to each eye (dichoptic). When components of the patterns had identical spatial frequencies, coherent motion was correctly perceived under dioptic and dichoptic viewing condition. However, the perceived direction deviated from the predicted direction when spatial frequency differences were introduced between components in both conditions. The results suggest that motion integration follows similar rules for dioptic and dichoptic plaids even though performance under dichoptic viewing did not reach dioptic levels. In the second experiment, the role of early cortical areas in the processing of both plaids was examined. As convergence of monocular inputs is needed for dichoptic perception, we tested the hypothesis that primary visual cortex (V1) is required for dichoptic plaid processing by delivering repetitive transcranial magnetic stimulation to this area. Ten minutes of magnetic stimulation disrupted subsequent dichoptic perception for approximately 15 min, whereas no significant changes were observed for dioptic plaid perception. Taken together, these findings suggest that V1 is not crucial for the processing of dioptic plaids but it is necessary for the binocular integration underlying dichoptic plaid motion perception.
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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".