Similar visual strategies are used to recognize spontaneous and posed facial expressions
Bibliographic record
Abstract
Most studies bearing on the visual strategies underlying facial expression recognition have been done using posed expressions (PE). However, evidence suggests that these expressions differ from spontaneous expressions (SE) in terms of appearance, at least in regard of intensity (Ekman & Friesen, 1969), and facial asymmetry (Ross & Pulusu, 2013). In this experiment, the Bubbles method (Gosselin & Schyns, 2001) was used to compare the facial features used to recognize both kinds of expressions. Twenty participants were asked to categorize SE and PE of four basic emotions (disgust, happiness, surprise, sadness). Pictures consisted of 21 identities taken from the MUG database (Aifanti et al., 2010). The amount of facial information needed to reach an accuracy rate of 63% was higher with SE (M=64.0, SD=15.6) than with PE (M=34.4, SD=8.7) [t(19)=-15.07, p< 0.001], indicating that SE were harder to recognize. Classification images of the facial features used by participants to recognize each emotion were generated separately for SE and PE. Statistical thresholds were found with the Stat4CI (Chauvin et al, 2005; Zcrit=3.0; p< 0.025). Similar features were used for the recognition of SE and PE of disgust, happiness and surprise, although the Z scores reached significantly higher values with PE. With the expression of sadness, the information contained in the eye region was only useful for PE. An ideal observer analysis confirmed that the most diagnostic features in the recognition of happiness, surprise and disgust are very similar for PE and SE, and that the eye area has a lower diagnosticity in spontaneous sadness. These results suggest that the facial features utilization underlying the recognition of SE and PE is very similar for most basic expressions, although some qualitative differences are observed for the expression of sadness. Meeting abstract presented at VSS 2017
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".