Facial age after-effects show partial identity invariance and transfer from hands to faces
Bibliographic record
Abstract
Background: While expression creates short-term dynamic changes in faces, age imparts a long-term dynamic change. In contrast to the work on expression, how facial age is represented in the human visual system has seldom been investigated with adaptation methods. Objective: As a parallel to our prior work on expression aftereffects, we studied the ability of age adaptation to transfer across face identity, face and non-face visual stimuli and sensory modality. Methods: We investigated facial age aftereffects using a perceptual bias paradigm in 48 healthy subjects. In a first experiment we examined whether face age aftereffects could be generated and how these were affected by changes in identity between adapting and test stimuli. In a second experiment, we asked whether hands, body silhouettes or body images at different extremes of age generated facial age aftereffects. In a final experiment, we asked whether young and old voices could do the same. Results: Age aftereffects were reduced but still significant when the identity of the face was changed between the adapting and test stimuli. Although body silhouettes and grayscale body images failed to generate age aftereffects in faces, we did find modest cross-stimulus transfer of age adaptation from hands to faces. There was no cross-modal transfer of aftereffects from voices to faces. Conclusions: The effects of identity on age aftereffects parallel our findings for the effects of identity on expression aftereffects, suggesting both identity-specific and identity-invariant components of age aftereffects. Transfer between hands and faces may reflect either the contribution of common properties like skin texture that may be potent age cues, or a convergence of representations at a visual semantic level.
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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 | 0.002 |
| 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) | 0.006 | 0.001 |
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".