Bibliographic record
Abstract
Cognitive aging has received growing attention in recent years as many researchers have documented a significant age-related decline in the brain’s processing ability. This decline could potentially undermine retirement security in two ways: 1) by limiting the ability to work longer; and 2) by eroding the capacity to manage finances in retirement. This brief summarizes the explosion of recent research on cognitive aging by answering basic questions about what researchers are learning and why their findings matter to retirement experts and the public. This overview is the first brief in a series of three; the other two will focus on how cognitive aging affects the ability of individuals to work between ages 50-70 and to handle personal finances between ages 70-90. The discussion proceeds as follows. The first section introduces definitions and measures of cognitive ability. The second section discusses how researchers identify changes in cognitive ability with age, while the third summarizes their findings. The fourth section discusses how age-related changes in different cognitive capacities can affect real-world performance. The final section concludes that: 1) most older workers can maintain their productivity up to age 70, although they will generally need more time to learn new skills or concepts; and 2) many retirees can continue to manage their own financial affairs in their 70s and 80s, though about one quarter will likely develop a cognitive impairment that will pose a threat to their financial independence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".