Training versus engagement as paths to cognitive enrichment with aging.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While a training model of cognitive intervention targets the improvement of particular skills through instruction and practice, an engagement model is based on the idea that being embedded in an intellectually and socially complex environment can impact cognition, perhaps even broadly, without explicit instruction. We contrasted these 2 models of cognitive enrichment by randomly assigning healthy older adults to a home-based inductive reasoning training program, a team-based competitive program in creative problem solving, or a wait-list control. As predicted, those in the training condition showed selective improvement in inductive reasoning. Those in the engagement condition, on the other hand, showed selective improvement in divergent thinking, a key ability exercised in creative problem solving. On average, then, both groups appeared to show ability-specific effects. However, moderators of change differed somewhat for those in the engagement and training interventions. Generally, those who started either intervention with a more positive cognitive profile showed more cognitive growth, suggesting that cognitive resources enabled individuals to take advantage of environmental enrichment. Only in the engagement condition did initial levels of openness and social network size moderate intervention effects on cognition, suggesting that comfort with novelty and an ability to manage social resources may be additional factors contributing to the capacity to take advantage of the environmental complexity associated with engagement. Collectively, these findings suggest that training and engagement models may offer alternative routes to cognitive resilience in late life.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it