The importance of static phase-aligned, high spatial frequency components for continuous flash suppression
Bibliographic record
Abstract
Purpose: A strong interocular suppression occur when counter-rich patterns continuously flash to one eye [Tsuchiya N., & Koch, C. (2005) Nature Neuroscience, 8 (8), 1096–1101]. This is called the continuous flash suppression. Here, we examined which aspects of these patterns are important for the continuous flash suppression. Methods: Observers viewed dichoptic images through a mirror stereo scope. Gabor patterns were presented as targets to one eye. Spatially filtered fractal noise patterns or checker board patterns were presented to the other eye with a flickering rate of 0 (no flicker) or 10 Hz. We measured contrast discrimination thresholds for targets across a range of monocular pedestal contrasts, with and without the dichoptic stimuli. Results and Discussion: Flicker by itself was not very effective in dichoptic suppression, neither were the low spatial frequency components of our fractal noise. The high spatial frequency components contributed the major suppressive effect even though its lowest component was at least 2 octaves from the signal frequency. To test for the importance of phase alignments at high frequencies we compared phase-aligned and phase-scrambled high-pass checkerboards and show the former to be more effective. These results suggest that phase alignment of the high spatial frequency components is critical for the continuous flash suppression. This research is funded by the Canadian Institutes of Health Research (MOP 53346 to RFH).
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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) | 0.002 | 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".