Specifying the Link Between Brain Integrity, Cognitive, and Affective Functioning in Aging Individuals
Bibliographic record
Abstract
This special issue presents methodologically innovative work that advances our understanding of the relationship between cognitive performance and affect, in particular highlighting the contribution of brain to behavior. Studies investigating the effects of aging on brain anatomy and physiology suggest targeted areas which are most affected by aging, and are characterized by cerebral atrophy, synaptic loss, changes in receptor numbers and function, among other anatomical changes. These changes are likely responsible for most of the observed age-related changes in cognitive function reported in many studies of aging. Identifying precisely how these brain changes affect cognition is a formidable challenge, though new testing methodologies, along with advances in neuroimaging analysis techniques, have led to testable hypotheses and models of the link between brain and behavior. A key question in the field of cognitive aging is whether we can identify factors that can account for the considerable variability that exists in cognitive decline across normal aging individuals. For example, research shows that extensive brain atrophy and synaptic density reduction can result in dementia symptoms in some individuals, while surprisingly, others are much more resilient and asymptomatic, despite having equivalent neural degradation (Katzman et al., 1988). Such protection from dementia symptoms has been linked to elevated “cognitive reserve,” defined as one’s efficiency at using existing neural circuits, and/or one’s flexibility in using compensatory mechanisms to accomplish cognitive tasks.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".