Escape from temporal-integration masking: The roles of visible persistence and input filtering.
Bibliographic record
Abstract
A brief target embedded in—and coterminating with—a noise mask is identified easily when the duration of the mask is long but not when it is short (Di Lollo, 1980; inverse-duration effect). Identification has been said to be mediated by the visible persistence of the target, which outlasted that of the mask. We tested an alternative account based on input filtering triggered by the onset and offset of the target, relative to those of the mask, without recourse to visible persistence. The results of Experiment 1 could not be explained wholly in terms of visible persistence but were entirely consistent with input filtering. Identification suffered in Experiment 2 when transient responses were attenuated by "ramping." In Experiment 3, accuracy improved gradually as a function of leading-mask duration. All results were consistent with a modified version of von Holst's (1954) hypothesis that a new stimulus (e.g., the present mask) establishes an input filter within the system. Any sudden onsets or offsets then lead to the perception of a new object only when they do not match the input filter, thus becoming segregated from the temporally leading stimulus.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".