The effects of intervening events between the two targets on the attentional blink
Bibliographic record
Abstract
Identification accuracy of the second of two targets (T2) is impaired when it is presented shortly after the first (T1).T1-based theories ascribe this attentional blink (AB) to a T1-initiated period of inattention.Distractor-based theories ascribe the AB to a disruption of input control caused by distractors trailing T1.The recent finding that an AB occurs in the absence of inter-target distractors seemingly disconfirms distractor-based theories.The principal goal of the present work was to explore the possibility that the blank inter-target interval itself may have disrupted attention, much like a distractor, thereby causing an AB.The intervening events between T1 and T2 were varied in four experiments (i.e., distractors, repeated T1, unexpected blanks, expected blanks).All produced an AB, disconfirming predictions from distractor-based theories, but lending strong support to the claim of T1-based theories that T1 processing alone is sufficient for the occurrence of the AB.Keywords: attention; attentional blink; T1-based theories; distractor-based theories; intervening events; inter-target distractors of many.First and foremost, I would like to thank my supervisors and mentors Dr. Thomas Spalek and Dr. Vincent Di Lollo for their invaluable enthusiasm, expertise and guidance and for giving me the opportunity to work in such a productive and enjoyable lab.I would also like to extend special thanks to my lab mates
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".