Featural processing in recognition of emotional facial 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 present study aimed to clarify the role played by the eye/brow and mouth areas in the recognition of the six basic emotions. In Experiment 1, accuracy was examined while participants viewed partial and full facial expressions; in Experiment 2, participants viewed full facial expressions while their eye movements were recorded. Recognition rates were consistent with previous research: happiness was highest and fear was lowest. The mouth and eye/brow areas were not equally important for the recognition of all emotions. More precisely, while the mouth was revealed to be important in the recognition of happiness and the eye/brow area of sadness, results are not as consistent for the other emotions. In Experiment 2, consistent with previous studies, the eyes/brows were fixated for longer periods than the mouth for all emotions. Again, variations occurred as a function of the emotions, the mouth having an important role in happiness and the eyes/brows in sadness. The general pattern of results for the other four emotions was inconsistent between the experiments as well as across different measures. The complexity of the results suggests that the recognition process of emotional facial expressions cannot be reduced to a simple feature processing or holistic processing for all emotions.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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