Attentional blink magnitude is predicted by the ability to keep irrelevant material out of working memory
Bibliographic record
Abstract
Participants have difficulty reporting the second of two masked targets if this second target is presented within 500 ms of the first target – an Attentional Blink (AB). Even unselected, healthy, young participants differ in the magnitude of their AB. Previous studies (Arnell, Stokes, MacLean & Gicante, 2010; Colzato, Spape, Pannebakker, – Hommel, 2007) have shown that individual differences in working memory performance using the OSPAN task can predict individual differences in AB magnitude where individuals with higher OSPAN scores show smaller ABs. Working memory performance also predicts AB magnitude over and above more capacity based memory measures which are unrelated to the AB (Arnell et al., 2010). Why might working memory performance predict the AB? One possibility is that individuals showing smaller ABs are better able to keep irrelevant information out of working memory. The present study employed an individual differences design, an AB task, and two visual working memory tasks to examine whether the ability to exclude irrelevant information from visual working memory (working memory filtering efficiency) could predict individual differences in the AB. Visual working memory capacity was positively related to filtering efficiency, but did not predict AB magnitude. However, the degree to which irrelevant stimuli were admitted into visual working memory (i.e., poor filtering efficiency) was positively correlated with AB magnitude over and above visual working memory capacity such that good filtering efficiency was associated with smaller ABs. Good filtering efficiency may benefit AB performance by not allowing irrelevant RSVP distractors to gain access to working memory.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".