Cognitive Development in Adulthood and AgingPreparation of this chapter was supported by a grant from the U.S. National Institute on Aging (R37 AG008235) to Roger A. Dixon, who also acknowledges support from the Canada Research Chairs program. Peggy McFall is supported by Alberta Innovates Health Solutions. Sanda Dolcos is now at the Department of Psychology, University of Illinois, Urbana, IL. This chapter is a substantially revised version of Dixon and Cohen .
Bibliographic record
Abstract
We discuss five aspects of cognitive development in adulthood and aging. Specifically, the chapter includes attention to (1) foundational issues in the study of cognitive change over many years of adulthood, (2) two enduring cognitive topics in the field, namely, intelligence and memory, (3) the long-standing but under-studied roles that biological and health conditions play in cognitive aging, (4) the recent and novel topic of emotional and affective influences on cognition throughout adulthood, and (5) the historically intriguing question of whether and how sustained cognitive health can be detected or promoted in normal aging.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".