Effects of Emotional Expression on Face Recognition May Be Accounted for by Image Similarity
Why this work is in the frame
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Bibliographic record
Abstract
We examined the degree to which differences in face recognition rates across emotional expression conditions varied concomitantly with differences in mean objective image similarity. Effects of emotional expression on face recognition performance were measured via an old/new recognition paradigm in which stimuli at both learning and testing had happy, neutral, and angry expressions. Results showed an advantage for faces learned with neutral expressions, as well as for angry faces at testing. Performance data was compared to three quantitative image-similarity indices. Findings showed that mean human performance was strongly correlated with mean image similarity, suggesting that the former may be at least partly explained by the latter. Our findings sound a cautionary note regarding the necessity of considering low-level stimulus properties as explanations for findings that otherwise may be prematurely attributed to higher order phenomena such as attention or emotional arousal.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it