Bibliographic record
Abstract
Humans communicate their emotions in large part through facial expressions. We developed a novel technique to study the static and dynamic aspects of facial expressions. The stimulus consisted of 4 parts: (1) a dynamic face, (2) two smaller versions of the starting and end states, (3) a label indicating the target dynamic expression, and (4) sliders that could be adjusted to change the facial characteristics. Participants were instructed to adjust the sliders such that the face would most closely match the target expression. Participants had access to 53 sliders, allowing them to manipulate static and dynamic characteristics such as face shape, eyebrow shape, mouth shape, and gaze. Preliminary data from 4 participants and 7 conditions revealed interesting effects. Some expressions are marked by unique facial features (e.g. anger given by frown, surprise and fright given by open mouth and open eyes, pain given by partially closed eyes, and happiness given by upwards curvature of the mouth). Some expressions seem to develop non-linearly in time, that is, include an intermediate state that deviates from a linear transformation between starting and ending states (e.g. anger, surprise, pain). This demonstrates the method's validity for measuring the optimal representation of given facial expressions. Because the method does no rely on presenting facial expressions taken from or derived from actors, we believe that it is a more direct measure of internal representations of emotional expressions. Current work is focused on building a vocabulary of emotions and emotional transitions, towards an understanding of the facial expression space.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".