Age‐related changes in the cerebral substrates of cognitive procedural learning
Bibliographic record
Abstract
Cognitive procedural learning occurs in three qualitatively different phases (cognitive, associative, and autonomous). At the beginning of this process, numerous cognitive functions are involved, subtended by distinct brain structures such as the prefrontal and parietal cortex and the cerebellum. As the learning progresses, these cognitive components are gradually replaced by psychomotor abilities, reflected by the increasing involvement of the cerebellum, thalamus, and occipital regions. In elderly subjects, although cognitive studies have revealed a learning effect, performance levels differ during the acquisition of a procedure. The effects of age on the learning of a cognitive procedure have not yet been examined using functional imaging. The aim of this study was therefore to characterize the cerebral substrates involved in the learning of a cognitive procedure, comparing a group of older subjects with young controls. For this purpose, we performed a positron emission tomography activation study using the Tower of Toronto task. A direct comparison of the two groups revealed the involvement of a similar network of brain regions at the beginning of learning (cognitive phase). However, the engagement of frontal and cingulate regions persisted in the older group as learning continued, whereas it ceased in the younger controls. We assume that this additional activation in the older group during the associative and autonomous phases reflected compensatory processes and the fact that some older subjects failed to fully automate the procedure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".