Visual masking with faces: Interruption of a trailing mask at critical SOA does not reduce masking.
Bibliographic record
Abstract
Modern theories of visual masking incorporate mechanisms that interefere with the consolidation of a target pattern into a conscious percept, and propose that feedback may be involved (Enns, 2004; Breitmeyer, 2007). To explore this, we conducted a series of experiments where we measured face identity discrimination thresholds in the central visual field under various masking conditions with face masks. In our first experiment (n=4), we examined masking as a function of SOA (stimulus onset asynchrony) in a standard backward masking paradigm, and compared it to common onset masking with a trailing mask. In this latter condition, the target and mask appeared at the same time, and after 33 ms, the target disappeared while the mask remained visible. In the SOA condition, peak masking occurred at an SOA of 58 ms, and in the trailing condition, masking equivalent to that of the peak SOA condition was found with a trailing mask duration of 58 ms and did not change as the trail was increased up to 600 ms. In Experiment 2, we tested seven observers in a modified trailing condition in which we briefly removed the masking stimulus for varying intervals, centered around the 58 ms point found to be critical in the previous SOA condition. When compared to an uninterrupted trail, we found no reduction in masking, with "mask gaps" as wide as 58 ms (p = 0.95). These results show that the effect of a trailing mask cannot be explained only by its presence at the critical SOA. We explore our findings in the framework of reverberant feedback loops. Meeting abstract presented at VSS 2014
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".