Visual search for faces with emotional expressions.
Bibliographic record
Abstract
The goal of this review is to critically examine contradictory findings in the study of visual search for emotionally expressive faces. Several key issues are addressed: Can emotional faces be processed preattentively and guide attention? What properties of these faces influence search efficiency? Is search moderated by the emotional state of the observer? The authors argue that the evidence is consistent with claims that (a) preattentive search processes are sensitive to and influenced by facial expressions of emotion, (b) attention guidance is influenced by a dynamic interplay of emotional and perceptual factors, and (c) visual search for emotional faces is influenced by the emotional state of the observer to some extent. The authors also argue that the way in which contextual factors interact to determine search performance needs to be explored further to draw sound conclusions about the precise influence of emotional expressions on search efficiency. Methodological considerations (e.g., set size, distractor background, task set) and ecological limitations of the visual search task are discussed. Finally, specific recommendations are made for future research directions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".