Jugement de l'authenticité des sourires et détection des indices faciaux.
Bibliographic record
Abstract
The smile is one of the most often expressed emotions during social interactions. It can be authentic, that is, associated with a joyful emotional state in the person expressing it, but it can also be false, that is, deliberately produced in the absence of that emotional state in order to deceive one or more individuals (Ekman, 1993). Even though the fake smile very much resembles the authentic smile, it generally does not constitute the perfect simile. The fake smile more often has a certain degree of asymmetry than the authentic smile (Ekman, Hager, & Friesen, 1981) and it uses the cheek raiser action less often than with the authentic smile (Ekman, Friesen, & O'Sullivan, 1988; Frank, Ekman, & Friesen, 1993). This study looked at the knowledge that adults have of these differences as well as their perceptive ability to detect them. The visual stimuli presented to participants were prepared using the Facial Action Coding System (Ekman & Friesen, 1978). Results show that participants detected the differences between the two types of smile and that detection was better using smile asymmetry than with the cheek raiser action. Analysis of the use of response categories in the detection task indicated that participants underestimated the differences between smiles when they were different and that this tendency was more apparent with the cheek raiser detection method than for asymmetry detection. Participants also demonstrated a better knowledge of smile asymmetry than cheek raiser action. The knowledge gathered suggests that the ability of the receptor to judge smile authenticity is limited by perceptive factors. However, the mediation analyses that we conducted show the judging smile authenticity is not limited to simple perceptive detection of facial clues. Detecting facial clues is a necessary condition for correctly assessing smile authenticity, but it does not explain the variance in these assessments. We believe that this variance would be due more to the importance that participants give to facial clues. Finally, our results show that the capacity to detect differences between authentic and fake smiles is not easy to change. Participants who received modified information on changes of appearance linked to the two facial parameters were not more likely to detect the differences than participants who did not receive information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".