Specific transfer effects following variable priority dual-task training in older adults
Bibliographic record
Abstract
PURPOSE: Past divided attention training studies in older adults have suggested that variable priority training (VPT) tends to show larger improvement than fixed priority training (FPT). However, it remains unclear whether VPT leads to larger transfer effects. METHODS: In this study, eighty-three older adults aged between 55 and 65 received five 1-hour sessions of VPT, FPT or of an active placebo. VPT and FPT subjects trained on a complex dual-task condition with variable stimulus timings in order to promote more flexible and self-guided strategies with regard to attentional priority devoted to the concurrent tasks. Real-time individualized feedback was provided to encourage improvement. The active placebo group attended computer classes. Near and far modality transfer tasks were used to assess the generalization of transfer effects. RESULTS: Results showed that VPT induced significantly larger transfer effects than FPT on a near modality transfer task. Evidence for larger transfer effects in VPT than FPT on a far modality transfer task was also observed. Furthermore, the superiority of VPT on FPT in transfer effects was specific to the ability to coordinate two concurrent tasks. CONCLUSIONS: Results of this study help better understand the benefits of VPT attentional training on transfer effects, which is an essential outcome for cognitive training effectiveness and relevancy.
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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) | 0.002 | 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".