Re-examining temporal selection errors during the attentional blink
Bibliographic record
Abstract
Two attentional episodes cannot occur very close in time. This is the traditional theory of the attentional blink, and it correctly predicts that the second of two successive attentional episodes often fails. But even when an episode succeeds, it may occur at an inappropriate time. Based on an analysis of response errors, Vul, Nieuwenstein and Kanwisher (2008) concluded that selection associated with a second target (T2) was temporally advanced for short lags and delayed for longer lags, and was less temporally precise during the blink period. However, their parametric estimates of attentional episode characteristics can be biased by instances in which the item reported for T2 was selected during an episode directed at the first target (T1). Such instances are evident in response error distributions, and could explain the phenomenon of lag-1 sparing. We reanalysed data from six studies, using mixture modelling to assess the characteristics of attentional episodes. At each lag, we compared two models: the first assumed that both target reports (T1 and T2) were drawn from a single attentional episode directed at T1; the second included an additional episode directed at T2. The results suggest that a second episode occurs only if lag exceeds 100250 ms, with the probability of initiating an episode returning to baseline for lags beyond about 500 ms. When a second episode does occur, the magnitude of its delay decreases as lag increases; but its temporal precision is invariant with lag, and is indistinguishable from a T1 baseline. This confirms that second attentional episodes are suppressed and delayed, but suggests that they are not temporally advanced for short lags, and that their temporal precision is not affected by earlier episodes. It also suggests that at least two items are sometimes retrieved from the first attentional episode, explaining lag-1 sparing. 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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".