The contribution of texture and shape to face aftereffects for identity versus age
Bibliographic record
Abstract
Background: Faces have both shape and texture, but the relative importance of the two in face representations is unclear. Objective: We determined the relative contribution of shape and texture to aftereffects for facial age and identity. We then assessed whether adaptation transferred from texture to shape and vice versa, to determine if these were integrated in a single representation. Methods: The first experiment examined age aftereffects. We obtained young and old images of two celebrities and created hybrid images, one combining the structure of the old face with the texture of the young face, the other combining the young structure with the old texture. This allowed us to create adaptation contrasts where structure was the same but texture differed between two adaptors, and vice versa. In the second experiment, we performed a similar study but this time examining identity aftereffects between two people of a similar age. In the last experiment, we used the normal and hybrid images to determine if adaptation to one property (i.e. texture) could create aftereffects in the perception of age in the other property (i.e. shape). Results: Both texture and shape generated significant age aftereffects, but texture contributed the majority of adaptation (77%). Both texture and shape also generated significant identity aftereffects, but the balance was different here, with texture accounting for only 32% of adaptation. In the last experiment, we found no transfer of age aftereffects between texture and shape. Conclusions: Shape and texture contribute differently to different face representations, with texture dominating for age and shape dominating for identity. The lack of adaptation transfer may indicate that these properties are encoded independently.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".