Prospects for Improving Cognition Throughout the Life Course
Bibliographic record
Abstract
Life expectancy is at an all-time high and is likely to continue to improve rapidly in the future (Wang & Preston, 2009); this, coupled with a modest birth rate, means that the proportion of older adults will continue to grow in the United States, with the strongest growth occurring in the number of ‘‘oldest old’’—those over the age of 85. Absent dramatically higher levels of immigration and higher rates of productivity growth, it is likely that all of us will either be consuming far less before and after retirement or working much longer than we might have expected. The current economic crisis has resulted in huge losses in financial assets including 401(k) retirement accounts; older workers close to retirement may choose to work much longer than they expected, while some of those already retired may try to return to the labor force. In this context, it has become imperative for us to preserve or enhance cognitive functioning among older adults and to compress the duration of any cognitive decline. But what can be done to prevent and remediate agerelated declines in cognition? Given the central role that cognition plays in determining an individual’s independence and well-being, this becomes a very serious question for research. Hertzog, Kramer, Wilson, and Lindenberger (2008, this issue) present what we believe is the most comprehensive review to date of the science of cognitive improvement in aging and present a clear picture of the barriers to progress in this area. Although they take a clear stand on the question of whether it is possible to remediate age-related cognitive decline (for the impatient, their answer is: Yes we can!), those holding opposing points of view will also find much value in this monograph. The National Institute on Aging (NIA) considers this topic to be one of paramount importance. In 2007, the NIA and the McKnight Brain Research Foundation cosponsored a Cognitive Aging Summit that prominently featured animated discussion of cognitive enhancement in aging (see http://www.health.ufl.edu/ brain/summit/index.htm for meeting materials). NIA’s research focus on enhancement spans many levels, from genes to cells to neural circuits to systems and on up through social engagement and societies. Hertzog and colleagues cover many of these levels in some detail, so we will only point out some selected areas that received less attention here and that could have important implications for the public interest and future research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".