How flexible and fast is the focus of attention? Evidence from the Attentional Blink and Lag-1 sparing
Bibliographic record
Abstract
When two targets (T1, T2) are inserted in a rapid stream of distractors, perception of T2 is impaired at short inter-target lags, a phenomenon known as the Attentional Blink. Identification accuracy for T2 is sometimes spared if T2 is presented directly after T1 (Lag-1 sparing). Research typically shows that Lag-1 sparing occurs only if the two targets appear in the same spatial location. It has recently been suggested, however, that the spatial relationship between the targets is not the determining factor; rather, Lag-1 sparing occurs whenever T2 appears within the focus of attention (Jefferies, Ghorashi, Kawahara, & Di Lollo, 2007; Jefferies & Di Lollo, 2009). According to this hypothesis, if the focus of attention shifts from the location of T1 to a second location, not only should Lag-1 sparing occur to T2 if it appears at the newly-attended location (Jefferies & Di Lollo, 2009), but Lag-1 sparing should comparably not occur to T2 at the now-unattended T1 location. The second half of this hypothesis – as yet unverified – is tested here by combining an attentional blink paradigm with a peripheral contingent capture paradigm. In Experiment 1, we found that when attention is shifted to a peripheral, task-relevant distractor, Lag-1 sparing occurred if T2 appeared at the newly-attended peripheral location, but not if it appeared at the central stream (i.e., the now-unattended location of T1). In Experiment 2, we found that if insufficient time is allowed for an attention shift to be completed, the focus of attention remains at the T1-location, and Lag-1 sparing is again found if T2 appears in the same location as T1. In summary, the current research tests and confirms the hypothesis that the critical determinant of Lag-1 sparing is T2 occurring within the focus of attention, not the targets appearing in the same spatial location.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".