Face Processing Changes in Normal Aging Revealed by fMRI Adaptation
Bibliographic record
Abstract
We investigated the neural correlates of facial processing changes in healthy aging using fMRI and an adaptation paradigm. In the scanner, participants were successively presented with faces that varied in identity, viewpoint, both, or neither and performed a head size detection task independent of identity or viewpoint. In right fusiform face area (FFA), older adults failed to show adaptation to the same face repeatedly presented in the same view, which elicited the most adaptation in young adults. We also performed a multivariate analysis to examine correlations between whole-brain activation patterns and behavioral performance in a face-matching task tested outside the scanner. Despite poor neural adaptation in right FFA, high-performing older adults engaged the same face-processing network as high-performing young adults across conditions, except the one presenting a same facial identity across different viewpoints. Low-performing older adults used this network to a lesser extent. Additionally, high-performing older adults uniquely recruited a set of areas related to better performance across all conditions, indicating age-specific involvement of this added network. This network did not include the core ventral face-processing areas but involved the left inferior occipital gyrus, frontal, and parietal regions. Although our adaptation results show that the neuronal representations of the core face-preferring areas become less selective with age, our multivariate analysis indicates that older adults utilize a distinct network of regions associated with better face matching performance, suggesting that engaging this network may compensate for deficiencies in ventral face processing regions.
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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".