Modeling the relationship between 3D facial features and human binary sex categorization in young Chinese adults
Bibliographic record
Abstract
New technologies, such as VR and AR, allow individuals to interact with digital avatars in increasingly realistic ways. As these applications become more widespread, it is important to understand how humans perceive 3D avatars and their facial features. People usually undergo a rapid unconscious cognitive sex categorization process when they begin to interact with new 3D characters. In this study, we used a sex categorization task to investigate how the human brain perceives 3D facial features under different conditions and to uncover how it carries out human binary sex categorization. With 20 young Chinese adult participants in our study, we aimed to reveal how key factors (3D facial shape, texture, and display method) affect human perceptions of binary sex and related facial feature differences. Using regression and PCA reconstruction, we developed a perceived sexual dimorphism model based on the participants' responses. The cosine similarity and difference modeling revealed a clear deviation from biological dimorphism, indicating that human perceptions simplify facial features, primarily focusing on the cheek region. Additionally, a repeated measures ANOVA and post hoc tests showed that dynamic videos with realistic textures yielded the most accurate sex categorization based on facial shape. In contrast, face stimuli lacking texture led to a noticeable male bias, indicating the importance of balanced texture on characters' faces. This work bridges a critical gap regarding human perceptions of 3D characters’ faces, offering valuable insights into creating 3D characters that are perceptually accurate and socially effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".