The sparing is far from spurious: Reevaluating within-trial contingency effects in the attentional blink.
Bibliographic record
Abstract
The attentional blink is the marked deficit in awareness of a 2nd target (T2) when it is presented shortly after the 1st target (T1) in a stream of distractors. When the distractors between T1 and T2 are replaced by even more targets, the attentional blink is reduced or absent, indicating that the attentional blink results from online selection mechanisms that act in response to distracting input rather than being the result of T1-induced cognitive resource depletion. However, Dell'Acqua, Jolicoeur, Luria, and Pluchino (2009) recently contended that an attentional blink is found in the multiple-target case as long as the appropriate trial context and analyses are used, thus reinstating resource-based explanations of the attentional blink and challenging the selection account. Specifically, an attentional blink reemerges when target performance is analyzed contingent on previous target accuracy. We argue on theoretical and empirical grounds that neither the trial context nor the type of analysis poses a serious problem for selection accounts. We show that the attentional blink and previous target contingency effects can be dissociated, with the latter depending more on low-level, short-range competition. We conclude that selection mechanisms involved in filtering for targets still provide a strong and coherent explanation of the attentional blink.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".