Verbal knowledge, working memory, and processing speed as predictors of verbal learning in older adults.
Bibliographic record
Abstract
The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables-speed of information processing, verbal knowledge, working memory-and the participant's age were included in the model in order to explain individual differences in the learning parameters. The data come from the second wave of the Zurich Longitudinal Study on Cognitive Aging (D. Zimprich, Martin, et al., 2008) comprising 334 participants ranging in age from 66 to 81 years (M = 74.43, SD = 4.41). Among the logistic, the Gompertz, and the hyperbolic function, the exponential function described the data best. Reliable individual differences were found in all 3 learning parameters. The cognitive predictor variables affected the verbal learning parameters differentially: All 3 predictors affected positively initial recall, the asymptotic performance increased with better working memory and faster processing speed, and the learning rate was positively associated with verbal knowledge only. Age did not affect the learning parameters but correlated negatively with working memory and processing speed. The finding of large and reliable individual differences in learning is seen as evidence that the potential for positive change, or plasticity in adulthood is maintained and that it is worthwhile to enhance the determinants of learning or learning itself.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".