Reconsidering Temporal Selection in the Attentional Blink
Bibliographic record
Abstract
Two episodes of attentional selection cannot occur very close in time. This is the traditional account of the attentional blink, whereby observers fail to report the second of two temporally proximal targets. Recent analyses have challenged this simple account, suggesting that attentional selection during the attentional blink is not only (a) suppressed, but also (b) temporally advanced then delayed, and (c) temporally diffused. Here, we reanalyzed six data sets using mixture modeling of report errors, and revealed much simpler dynamics. Exposing a problem inherent in previous analyses, we found evidence of a second attentional episode only when the second target (T2) follows the first (T1) by more than 100 to 250 ms. When a second episode occurs, suppression and delay reduce steadily as lag increases and temporal precision is stable. At shorter lags, both targets are reported from a single episode, which explains why T2 can escape the attentional blink when it immediately follows T1 (Lag-1 sparing).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".