Social attention as a general mechanism? Demonstrating the influence of stimulus content factors on social attentional biasing.
Bibliographic record
Abstract
Humans spontaneously attend to faces and eyes. Recent findings, however, suggest that this social attentional biasing may not be driven by the social value of faces but by general factors, like stimulus content, visual context, or task settings. Here, we investigated whether the stimulus content factors of global luminance, featural configuration, and perceived attractiveness may independently drive social attentional biasing. Six experiments were run. In each, participants completed a dot-probe task where the presentation of a face, a house, and two neutral images was followed by the presentation of a response target at one of those locations. Experiments 1 and 2 assessed social attentional biasing when the face had higher overall global luminance. Experiments 3 and 4 assessed social attentional biasing when the face (but not the comparison house) retained the typical canonical configuration of internal features. Experiments 5 and 6 examined social attentional biasing when the face was more attractive than the house. Experiments 1, 3, and 5 measured manual responses when participants were instructed to maintain fixation. Experiments 2, 4, and 6 measured both manual and oculomotor responses when no instructions about eye movements were provided. The results indicated no reliable social attentional biasing in Experiments 1 to 5, however, a reliable saccadic bias toward the eyes of attractive upright faces was found in Experiment 6. Together, these results show that perceived facial attractiveness may be an important general factor in social attentional biasing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".