Social monitoring: The psychophysics of facial communication
Bibliographic record
Abstract
We often rely on the facial expressions of others to determine our response to events they can see, but we cannot (e.g., a dog barking behind you likely poses no threat if the person facing you is smiling). Recent advances in personal computing now make it convenient to study social monitoring in new ways. Specifically, the video camera atop the screen makes it possible to record the dynamic expressions of participants otherwise engaged in the tasks studied by cognitive scientists. The video clips of these facial expressions can then be used as stimuli in their own right to study social monitoring processes and abilities. In our lab we began by selecting 80 photos from the IAP set (Lang et al., 2005) that varied in valence (negative vs. positive) and arousal (low vs. high). In Part 1 the 80 images were presented in a random order for 3 sec, with participants viewing the complete set three times. The first time no mention was made of facial expressions. Participants were told the camera would record where they were looking while they categorized images as negative or positive. The second time they were asked to deliberately make expressions that would convey the emotional tone of the picture to someone else. The third time they were asked to make expressions that would mislead someone regarding the picture. In Part 2 the video clips from these three phases were used to answer several questions about social monitoring: Which IAP pictures result in reliable spontaneous expressions that convey emotions to viewers? Does reading someone's facial expression improve with training through feedback? How easy is it to discriminate genuine from faked expressions? Answers to these and other questions will be presented in discussing how to harness this new technology in the study of social-cognitive perception.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".