Dynamic properties in broadband pattern masking: Comparison between monocular, binocular and dichoptic viewing conditions.
Bibliographic record
Abstract
To assess how remote spatial frequency components of the mask influence the pattern masking in temporal domain, the masking effect was measured under various presentation duration (50, 100, 250 and 1000ms) in monocular, binocular and dichoptic viewing conditions. The target was a horizontal sine-wave grating with spatial frequency of 1 cpd. Three types of masks with the same fundamental spatial frequency were used: a sine-wave grating(S), a square-wave grating (Q), and a missing fundamental square-wave grating (M). The contrast of the mask was set at either 0% or 40%. A spatial four-alternative-force-choice was used to measure the target threshold. The results showed the presentation duration had no main masking effect, suggesting the masking occurs fast, at least within 50ms. Under monocular and binocular viewing conditions, the Q mask caused a stronger masking effect than the S masks and the M masks also caused significant masking. Under dichoptic viewing, on the contrary, the S masks caused stronger masking than the Q masks and masking effect also occurred in dichoptic M mask. These results suggested that the contributions of remote spatial frequencies are rapid, occurring within 50ms and differ under monocular and dichoptic viewing. Meeting abstract presented at VSS 2013
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".