Encoding of age-invariant identity versus identity-invariant age from faces: An fMRI-adaptation study
Bibliographic record
Abstract
Humans can both estimate the age of people from their faces and recognize the same individual at different times of life. This indicates an ability to perceive age-related characteristics that generalize across identity, and also an ability to derive age-invariant representations of facial identity. We investigated the degree to which either or both of these abilities reflected the operation of processes within the fusiform face areas (FFA), of the left and right hemispheres, through the use of an fMRI adaptation paradigm. Our stimuli were 3D avatar faces created with FaceGen software. Ten different individual male faces were chosen with the aim of maximizing the perceived differences between faces. We created images of each face at 10 different ages ranging from 20 to 60 years. Eleven healthy subjects participated in this study. First, an FFA in both the right and left hemisphere was identified in each individual using a functional localizer that contrasted blocks of viewed objects with blocks of viewed faces. Following the localizer, subjects underwent a block-design adaptation run consisting of three experimental conditions; blocks of avatar faces which differed in both identity and age, blocks of avatar faces which differed in identity but all of the same age, and blocks of the same avatar face at different ages. We found that adaptation for identity regardless of variations in age occurred most strongly in the left FFA (p[[lt]]0.00003), with a trend to a similar effect in the right FFA (p= 0.09). Neither the left or right FFA showed adaptation effects for age. We conclude that age-invariant representations for face identity may be encoded within the FFA, possibly predominantly within the left hemisphere, and that representations of age-related characteristics of faces may be encoded elsewhere in the face-processing network.
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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 | 0.001 |
| 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) | 0.001 | 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".