P1‐416: Individual differences in cognitive plasticity and variability as predictors of cognitive function in older adults
Bibliographic record
Abstract
With the growth in elderly populations worldwide, there is a pressing need to characterize the changes in cognition and brain function across the adult lifespan. The evolution of cognitive abilities is no longer considered to reflect a universal, cumulative process of decline. Rather, significant inter- and intra-individual differences exist in cognitive trajectories, with the maintenance of functions ultimately determined by multi-dimensional biological and psychological processes. The current study examined the relationship between intra-individual variability, cognitive plasticity, and long-term cognitive function in older adults. Data were analyzed from Project Mental Inconsistency in Normals & Dementia (MIND), a 6-year longitudinal burst design study, integrating micro-weekly assessments (reaction time (RT) tasks), with macro-annual evaluations (cognitive outcome measures). Participants included 304 community-dwelling adults, ranging in age from 64 to 92 years (M = 74.02, SD = 5.95). Hierarchical multiple regression models were developed to examine long-term cognitive function, along with multilevel modeling (HLM) techniques for the analysis of specific predictors of longitudinal rates of cognitive change. Baseline intraindividual variability (ISD) emerged as a robust and highly sensitive predictor, with increased variability associated with decreased long-term cognitive performance. Complex baseline cognitive plasticity (1-Back 4-Choice RT Task) uniquely predicted subsequent cognitive function for measures of processing speed, fluid reasoning, episodic memory, and crystallized verbal ability. Multilevel models revealed chronological age to be a significant predictor across cognitive domains, while intraindividual variability selectively predicted rates of change for performance on measures of episodic memory and crystallized verbal ability. These findings underscore the potential utility of intraindividual variability and cognitive plasticity as dynamic predictors of longitudinal change in older adults.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".