Do emotionally expressive faces automatically capture attention? Evidence from global–local interference
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The present experiments investigated whether perception of a global face gestalt automatically interferes with processing of facial features. Upward- and downward-curved arcs were grouped into triplets to resemble faces with positive or negative expressions. The arcs were presented either in a uniform grey colour to facilitate global face perception or in mixed colours where individual arcs were coloured red to reduce global face perception. Experiments 1 and 2 induced a local processing orientation by requiring participants to count individual arc features. Negative face displays yielded slower and less accurate arc counting performance than positive face displays, but only when all arcs were the same colour. In Experiment 3, a global processing orientation was induced by requiring participants to count the number of arc triplets. This time, negative face displays yielded slower reaction times, regardless of feature colour. These results show that interference from emotional face gestalts is not automatic but can be eliminated and may depend on both attentional control settings and “bottom-up” stimulus attributes. Acknowledgements This research was supported by grants to JDE and DS from the Natural Sciences and Engineering Research Council. The first and last authors contributed equally to the present work. Notes 1It should be noted that the analyses yielded equivalent results even when the data of those 18 participants were not removed.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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