Visual search for faces with emotional expressions.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.009 | 0.005 |
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