Exogenous reconfiguration of the input filter: When it happens and when it does not
Bibliographic record
Abstract
When two targets (T1, T2) are inserted in a stream of distractors, accuracy in identifying T2 is impaired if the interval between T1 and T2 is short. Di Lollo et al., (2005) proposed that this T2-deficit, known as the attentional blink (AB), results from a temporary loss of control over the current attentional set. Specifically, when the system is processing T1, it is vulnerable to an exogenously-triggered switch in attentional set caused by the items following T1. This exogenous filter reconfiguration leaves the system poorly prepared for T2 (if the items do not match) or well prepared (if the items match), thereby influencing the magnitude of the T2-deficit. The present study tested the limits of this system configuration process. Observers were presented with targets from a set of numbers and letters (1,2,3,A,B,C). Because targets were of both types, observers could not prepare optimally to select items based on class membership; each had to be coded separately. We varied whether the targets matched in class (numbers vs. letters) and whether the items intervening the targets were numbers or letters. An AB deficit was observed in all conditions, with no effect of the similarity between intervening items and T2. This finding establishes a clear limit on the nature of the task for which an input filter can be set optimally, and on when the system is vulnerable to exogenous reconfiguration. Additional experiments examined the conditions under which optimal task filters can be prepared in the perception of targets in rapid visual streams.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".