Properties of shape interaction in temporal masking
Bibliographic record
Abstract
The perceived shape of a pattern (target) can be masked by that of a subsequent pattern (mask). The purpose of the present work is to elucidate the nature of this shape masking. Perceived deformation of a target radial frequency (RF) pattern (radius 1.1deg) was measured when followed by an RF mask (radius 1.6 deg) of high amplitude (16 × detection threshold). The effect reaches a maximum at a stimulus onset asynchrony (SOA) of 80–100 ms, where thresholds are elevated by a factor of 16. Various configurations in which target and mask are separated by this SOA were tested (the mask is termed primary here). Conditions in which a second mask is interleaved in time between the target and primary mask, lead to smaller threshold elevations and reveal that the onset of the mask that appears first in time determines the magnitude of masking. Configurations in which apparent motion is possible between the target and both masks lead to large threshold elevations (factors of 16–20) and demonstrate that both masks contribute to the effect. The magnitude of these effects is much larger than that predicted by a combination of spatial lateral interactions and apparent motion between target and mask. Results suggest that shape, apparent motion, and stimulus onset play an interactive role in masking, and that target shape is not determined at initial onset, but rather, is extrapolated (postdictively) after a window of 80–100ms.
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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 | 0.003 |
| 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".