The effectiveness of unitization in mitigating age-related relational learning impairments depends on existing cognitive status
Bibliographic record
Abstract
Binding relations among items in the transverse patterning (TP) task is dependent on the integrity of the hippocampus and its extended network. Older adults have impaired TP learning, corresponding to age-related reductions in hippocampal volumes. Unitization is a training strategy that can mitigate TP impairments in amnesia by reducing reliance on hippocampal-dependent relational binding and increasing reliance on fused representations. Here we examined whether healthy older adults and those showing early signs of cognitive decline would also benefit from unitization. Although both groups of older adults had neuropsychological performance within the healthy range, their TP learning differed both under standard and unitized training conditions. Healthy older adults with impaired TP learning under standard training benefited from unitized training. Older adults who failed the Montreal Cognitive Assessment (MoCA) showed greater impairments under standard conditions, and showed no evidence of improvement with unitization. These individuals' failures to benefit from unitization may be a consequence of early deficits not seen in older adults who pass the MoCA.
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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 | 0.001 |
| 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 | 0.000 |
| 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".