The Effect of Aging on Face-Name Recognition: An fMRI Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to detect functional changes in the brain during the memory task with aging and the association between functional changes and memory performance. METHOD: The study consisted of Young Adult Group (YAG, n=20) aged 20 to 25 and Late Adult Group (LAG, n=18) aged 60 to 70. Individuals with Montreal Cognitive Assessment (MoCA) scores above 21 and no family history of Alzheimer's Disease were included in the study. Functional Magnetic Resonance Imaging (fMRI) scanning was performed on all participants during a memory task including encoding (face and name), face and name recognition sub-tasks. RESULTS: Results indicated that LAG showed increased activity during face recognition task in left posterior cingulate cortex, left superior frontal cortex, left fusiform face area and another increased activity was found out during name recognition task in left superior frontal cortex, right prefrontal cortex, left anterior + posterior cingulate cortex. The accuracy of face recognition and name recognition memory tests were significantly lower in LAG (respectively, p=0.026; p=0.001). CONCLUSION: These results indicated that advanced age were associated with more widespread activation in brain during memory task. Thus with aging, individuals require more neuronal and cognitive resources during memory processing.
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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 | 0.000 |
| 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".