Fear Factor: Attention capture by fearfully expressive faces in an RSVP task
Bibliographic record
Abstract
When participants search rapid serial visual presentation (RSVP) streams for a single target, accuracy is high, given that distractors in RSVP do not usually deplete the attentional resources required to perform the target search. Attentional blink (AB) occurs when RSVP streams include two targets. Accuracy for identification of the first target (T1) is typically high, but accuracy for the second target (T2) is impaired if it is presented less than 500 ms after the first target (Raymond, Shapiro, & Arnell, 1992). However, researchers have demonstrated that a task-irrelevant RSVP distractor can act as an involuntary T1 and result in an AB provided that it adequately matches the target search template or is visually novel. Task-irrelevant T1 arousing words capture attention and enter awareness at the expense of T2 targets (Arnell, Killman, Fijavs, 2007). In the present experiment, participants performed single target T2 search for recognition of an intact scene imbedded among 16 scrambled scenes and faces. The task-irrelevant T1 distractor image of an intact face varied in emotional expression (happy, fear or neutral) and appeared 270ms (within the AB window) or 630ms (outside the AB window) before the T2 target scene onset. We also included a control condition with no T1 distractor. RSVP stream images were presented for 90 ms each. Participants performed a 4-AFC scene matching task following the RSVP stream. Preliminary results indicate significantly poorer accuracy for identification of scenes preceded by a task-irrelevant T1 face image presented at 270 ms compared to 630 ms but only for fearful emotional expressions. The emotional expression, happy, did not cause AB. This suggests that fearful emotional expressions capture attention and enter awareness at the expense of goal-driven targets, signifying preferential attentional processing perhaps as an evolutionary self preservation mechanism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".