Is emotion perception altered by gaze direction, gender appearance, and gender identity of the perceived face?
Bibliographic record
Abstract
The purpose of the present study was to examine how gaze and emotion processing may change due to differences in gender appearance and gender identity of the perceived face. We manipulated gender appearance (male or female), gender identity (cisgender or transgender), gaze direction (direct or averted), and expressed emotions (anger, fear, or neutral) of face models in an emotion rating task. We replicate several previous findings, including a direct gaze advantage, an emotion effect, and an interaction between gaze direction and expressed emotion. In line with previous findings on the influence of facial morphology for face processing, we found that male faces were more quickly and intensely perceived for displays of anger, while female faces were more quickly and intensely perceived for displays of fear. Of key interest, gender identity influenced face perception for different emotion expressions and gaze directions for ratings and reaction times in a variety of ways. For example, transgender male faces were seen as angrier and less fearful than cisgender male faces, while the opposite effect occurred for female faces. These results suggest that face perception is systematically shaped by morphological differences as well as more abstract social constructs related to gender identity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".