Irrelevant faces do not capture spatial attention in RSVP sequences
Bibliographic record
Abstract
Emotional faces are very engaging and are proposed to capture attention in spatial tasks (Eastwood, Smilek, & Merikle, 2003). Uniquely-coloured distractors can also capture attention, and can do so even when attention is maintained in a highly-focused state at a known target location, such as when identifying a target letter from an RSVP sequence of distractor letters (Folk, Leber, & Egeth, 2002). Here we examine whether emotional faces can capture attention when attention is highly focused at a known target location. In Experiment 1 a sequence of black letters was presented in an RSVP sequence. Two straight lines and a curve, which either formed an emotional face or a meaningless group (T1) surrounded one black letter. Subjects reported the identity of one red letter (T2) presented at various temporal lags from T1. Attention was captured by the perceptual groups only when they were red, leading to impaired T2 identification at short lags; distractors forming an emotional face, however, were no more detrimental to T2 identification. In Experiment 2 distractor letters were omitted from the sequence in the positions before, during, and immediately after the perceptual group. Meaning again did not influence T2 identification. In Experiment 3 subjects performed visual search for a target (a perceptual group, or a letter within a perceptual group), presented in a static array with other meaningless distractor perceptual groups, one of which sometimes formed an emotional face. The presence of an emotional face slowed response times for letter targets within perceptual groups, but not for perceptual groups themselves. The results indicate that a strict filter is adopted by the attentional system to cope with the heavy demands imposed by the RSVP paradigm. Consequently, emotional faces do not capture attention when it is narrowly focused, but do so when attention is broadly distributed. Meeting abstract presented at VSS 2012
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".