That's my name, don't wear it out: Attentional blink and the cocktail party effect
Bibliographic record
Abstract
When individuals are asked to identify two targets in an RSVP task, accuracy on the second target (T2) is reduced if presented shortly after the first target (T1) — an attentional blink (AB). Previous research has shown that sexual words can increase the magnitude of the AB when presented as T1, set off an AB as a distractor, and can overcome the AB when presented as T2. When an individual's name is presented, it too can set off an AB as a distractor, and overcome the AB when presented as T2, but one study has shown that the AB is not affected when a person's name is presented as T1 (Shapiro et al., 1997). As sexual words and personal names are especially salient stimuli, it is surprising that own names do not create a larger AB when presented as T1. To examine this, we used an AB task where we presented own names, other names, or nouns as T1, and neutral colour names as T2. The AB was significantly increased for own names as compared to other names and nouns, but only for the first 15 presentations of the name. For each participant there is only one own name, yet sets of over 20 sexual words have been used in previous studies. Therefore, sexual words may be able to show effects over 20 times more trials than own names. Indeed, when we examined the impact of own names presented as T1 to a single sexual word presented as T1, the AB magnitude and the number of trials before the effects disappeared were comparable. We conclude that own names are salient stimuli and increase the AB when presented as T1, but that this effect rapidly disappears due to the fact that only one stimulus can be used in the own name condition.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".