Limited Benefits of Heterogeneous Dual-Task Training on Transfer Effects in Older Adults
Bibliographic record
Abstract
OBJECTIVES: It has often been reported that cognitive training has limited transfer effects. The present study addresses training context variability as a factor that could increase transfer effects, as well as the manifestation through time of transfer effects. METHOD: Fifty-eight older adults were assigned to an active placebo or two dual-task training conditions, one in which the training context varies between sessions (heterogeneous training) and the other in a fixed training context (homogeneous training). Transfer was assessed with near and far-modality transfer tasks. RESULTS: Results show that heterogeneous and homogeneous training led to larger near-modality transfer effects than an active placebo (computer lessons). Transfer effects were roughly comparable in both training groups, but heterogeneous training led to a steeper improvement of the dual-task coordination learning curve within training sessions. Also, results indicated that dual-task cost did not improve in the active placebo group from the pre- to the post-training sessions. DISCUSSION: Heterogeneous training showed modest advantages over homogeneous training. Results also suggest that transfer effects on dual-task cost induced by training take place early on in the post-training session. These findings provide valuable insights on benefits arising from variability in the training protocol for maximizing transfer effects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".