On the costs of lag-1 sparing.
Bibliographic record
Abstract
The attentional blink (AB) is a dual-target, rapid serial visual presentation (RSVP) deficit thought to represent a failure of perceptual awareness that reflects the dynamics of temporal attention. However, second target (T2) report is typically unimpaired when the targets appear within 150 ms of one another (i.e., lag-1 sparing). In addition, this sparing can be extended if more targets appear sequentially. It is thought that sequential targets are processed in the same attentional window. Here, we investigated the fate of targets processed in these windows and, specifically, the consequence for subsequent targets when an item at lag-1 is reported versus missed. The results demonstrated that target encoding in attentional windows has an all-or-none influence on subsequent item report: When comparing two- and three-target (T1 and T2 not separated by distractors) RSVP streams, there was no difference in AB magnitude for the final target when either T2 or T1 was missed in the three-target condition, but both of these conditions had significantly smaller blinks than those observed when T1 and T2 were accurately reported. A comparison of our results to a computational model of temporal attention demonstrates how structural limitations on the rate of encoding affect perception, even during sparing.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".