On the interactive influence of facial appearance and explicit knowledge in social categorization
Bibliographic record
Abstract
Abstract Although people form impressions of others with ease, sometimes one's initial perceptions of individuals conflict with what one knows about them. Here, we aimed to investigate the process by which explicit knowledge about people interacts with initial perceptions on the basis of cues from facial appearance. Participants memorized the sexual orientations of men's faces wherein half of the targets were encoded with a sexual orientation opposite to their actual orientation. Subsequent categorization showed that perceivers favored appearance‐based information when temporally constrained but favored explicit knowledge about group membership with increased viewing time. Additionally, real‐time measures of participants' categorizations showed greater vacillation between appearance‐based cues and explicit knowledge as viewing time increased. These findings suggest that explicit knowledge does not simply overrule appearance‐based cues past a particular threshold but that the two may interact recurrently with top‐down knowledge directing attention and perception at later processing. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".