Diferenças entre sexo no reconhecimento de expressões faciais de emoção
Bibliographic record
Abstract
Resumo Expressões faciais são fundamentais nas interações sociais podendo ser percebidas diferentemente de acordo com emoção, intensidade e sexo. Neste estudo, 903 participantes responderam a uma tarefa de reconhecimento de expressões faciais de emoção (REFE) com 26 faces criadas digitalmente (50% femininas), sendo uma face neutra e as demais expressando alegria, medo, nojo, raiva, tristeza e surpresa, com intensidades de 30% e 70%. Comparamos homens e mulheres quanto a identificação correta da emoção, tempo de resposta e intensidade atribuída a emoção. Homens tiveram maior acurácia, rapidez e atribuíram mais intensidade em algumas emoções, particularmente alegria. Mulheres tiveram mais acurácia rapidez e atribuíram maior intensidade a outro conjunto de emoções. Portanto, não suportamos a hipótese de superioridade feminina no REFE.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.009 |
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; both teacher heads agree on what is shown here.
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".