The "working" component of working memory predicts AB magnitude
Bibliographic record
Abstract
In rapid serial visual presentation (RSVP) second target (T2) accuracy decreases with temporal proximity to the first target (T1). This phenomenon is known as the attentional blink (AB). Colzato et al. (2007) used an individual differences approach to examine whether individual AB magnitude was predicted by working memory (WM) operation span, using the Operation Word Span paradigm (OSPAN), and general cognitive ability, measured with Raven's Standard Progressive Matrices (SPM). They found that OSPAN score inversely correlated with AB magnitude even with Raven's SPM partialled out. However, it is not clear from this study whether active “working” memory ability would predict AB magnitude better than a less dynamic measure of short-term memory capacity. Using a continuum of WM measures that reflect varying degrees of the active “working” process could better define the source of the relationship. The digit-forward task is a simple rote memory task that reflects static STM capacity, requiring individuals to repeat a string of digits in order of presentation. The digit-backward task requires an increasing degree of the active “working” component by having individuals repeat the string of digits in the opposite order of presentation. Our study used these measures plus a reliable measure of individual visual memory capacity (k) in addition to the OSPAN; as well as Raven's SPM and a reading comprehension task to look at as predictors of individual AB magnitude. Dramatically, the OSPAN still inversely correlates with AB magnitude with Raven's SPM, reading comprehension, and digit- forward and backward partialled out. This is strong support that a “working”, executive component of WM predicts temporal limitations of selective attention beyond static STM capacity and general cognitive ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".