Bibliographic record
Abstract
The number of reports on the cognitive neuroscience of aging has increased in recent years, and most of these studies have found many similarities in the patterns of activity in young and old adults, indicating that basic neural mechanisms are maintained into older age. Despite these overall similarities, older adults often have less activity in some regions, such as medial temporal areas during memory processing and visual regions across a variety of cognitive domains. It seems clear that age reductions in cognitive function can be tied, at least in part, to these reductions in brain activity. On the other hand, older adults typically also overrecruit some brain areas, mainly the ventral or dorsal prefrontal cortex during memory tasks, as well as both the frontal and parietal regions during tasks engaging cognitive control processes, such as attention. Sometimes this overrecruitment appears to be in response to altered function in other brain regions and is often seen in those older adults who perform better on the task at hand. These findings have provided rather convincing support for the idea that overrecruitment can be compensatory in the elderly. Nevertheless, not all age increases can be interpreted as compensatory, and some are more indicative of neural inefficiency. The challenge facing future research will be to understand the task conditions that promote compensation in older adults, the role of the various brain areas in aiding cognitive function, and how these compensatory mechanisms can be elicited to enhance quality of life in the elderly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".