Gender Differences in Perceiving Avatar Face and Interpersonal Distance: Exploring Realism and Social Presence in Mixed Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding gender differences in facial and spatial recognition is crucial for enhancing avatar-mediated communication. However, there remains a gap in understanding how participant gender influences perceptions of avatar facial expressions and spatial dynamics in Mixed Reality communication. Therefore, our study investigates how avatar non-verbal cues interact with gender differences to affect user experience and understanding in MR environments. To examine these complex relationships, we conducted a user study comparing the effects of various avatar facial expressions (Full, Mouth-Only, and Emotion-based) and interpersonal distances (Closer vs. Farther) on facial animation realism and social presence, with a focus on gender-balanced participant groups. Our findings revealed that female participants were particularly sensitive to the avatar’s proximity and facial expressions, reporting significantly higher perceptions of facial animation realism, copresence, message understanding, and affective understanding at farther distances compared to male participants. They also perceived higher copresence and message understanding when exposed to emotion-based facial expressions, as opposed to a mouth-only condition-a distinction not observed among male participants. Based on our findings, we advocate for avatar design strategies that accommodate gender differences in perception and preference, potentially through customizable levels of expressiveness to cater to diverse user needs and contexts.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it