Dual perceptual adaptation in human faces: Gender and age
Bibliographic record
Abstract
Purpose: Adaptation to female faces makes a gender-neutral test face appear male, and vice versa. While it may not be clear which features define “maleness” or “femaleness” in faces (Webster et al., 2004) or biological motion (Jordan, Fallah & Stoner, 2006), the assumption is that adaptation shifts the viewer's judgment along a single perceptual dimension. Perceptual adaptation effects have been extended beyond gender to many other dimensions depicted by faces, e.g. identity, race, viewpoint, expression, attractiveness etc. However, it remains unknown whether perceptual adaptation can occur for more than a single dimension. Method and Results: The first study tested whether gender adaptation is observed to children's faces. On each block of trials, participants were adapted to either boy or girl faces for a period before judging a morphed test face as predominantly a boy or a girl. Participants were more likely to report gender neutral stimuli as a girl after adaptation to the faces of boys and vice versa. This clearly replicates studies showing gender adaptation for adult faces. Like adults, the faces of boys and girls appear to be represented along a single gender dimension. The second study tested the relationship between the representation of young (boys/girls) and mature (men/women) males and females. The adapter and test stimuli comprised all possible pairs of gender/age combinations. Adaptation was observed simultaneously across both gender and age. The relationship between age and gender adaptation effects, and its implications for neuronal representation will be discussed.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".