Differences in Attentional Involvement Underlying the Perception of Distinctive and Typical Faces
Why this work is in the frame
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Bibliographic record
Abstract
Differences in human faces can be evaluated along a continuum that ranges from 'distinctive' to 'typical.' We examined processing differences between distinctive and typical faces by two attentional tasks that induce attentional blink (AB). Given that AB is believed to reflect temporal or capacity limits of attention, stimuli that survive AB are believed to be associated with greater processing efficiency. In a change-detection task, participants were required to detect changes in the two pairs of faces that were presented in rapid succession. Changes involving the distinctive face of a pair were more likely to be detected than those involving a typical face. In a face-identification task, distinctive faces embedded in a rapid serial visual presentation (RSVP) stream were identified with a greater accuracy than typical faces. Together, our results suggest that distinctive faces are associated with greater processing efficiency and may be explained in terms of perceptual salience, a stimulus dimension known to attract attention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it