Cognitive ageing: a positive perspective
Bibliographic record
Abstract
Ageing is characterized by decreased brain volume, changes in general neuronal efficacy and connectivity and by a number of medical conditions, any or all of which contribute to widely reported age-related declines in cognition and memory. However, a number of findings in the recent literature suggest that the age-related declines do not characterize all of cognition, and that there may even be domains in which older adults outperform younger adults. We offer an overview of this evidence along with a review of ways in which standard laboratory procedures may be biased against older adults, leading to an underestimation of their true abilities, as well as to an overestimation of the magnitude of age differences. These two sections raise questions regarding how brain functions compensate in the face of widely reported neurobiological differences with age, and suggest that the full abilities of older adults have yet to be recognized. Introduction On 15 January 2009, Captain Chesley Sullenberger landed an engineless plane in the Hudson River, saving the lives of all 154 people aboard. Similar dramatic rescues of crippled aircraft have occurred over the years and most have had one thing in common: a highly experienced pilot was flying the plane. Professional pilots agreed that it was the training and experience of these pilots that enabled them to respond successfully to the extreme challenges their planes faced.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".