Decomposing the relationship between cognitive functioning and self-referent memory beliefs in older adulthood: what’s memory got to do with it?
Bibliographic record
Abstract
With advancing age, episodic memory performance shows marked declines along with concurrent reports of lower subjective memory beliefs. Given that normative age-related declines in episodic memory co-occur with declines in other cognitive domains, we examined the relationship between memory beliefs and multiple domains of cognitive functioning. Confirmatory bi-factor structural equation models were used to parse the shared and independent variance among factors representing episodic memory, psychomotor speed, and executive reasoning in one large cohort study (Senior Odyssey, N = 462), and replicated using another large cohort of healthy older adults (ACTIVE, N = 2802). Accounting for a general fluid cognitive functioning factor (comprised of the shared variance among measures of episodic memory, speed, and reasoning) attenuated the relationship between objective memory performance and subjective memory beliefs in both samples. Moreover, the general cognitive functioning factor was the strongest predictor of memory beliefs in both samples. These findings are consistent with the notion that dispositional memory beliefs may reflect perceptions of cognition more broadly. This may be one reason why memory beliefs have broad predictive validity for interventions that target fluid cognitive ability.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".