An Investigation of Response and Stimulus Modality Transfer Effects after Dual-Task Training in Younger and Older
Bibliographic record
Abstract
It has been shown that dual-task training leads to significant improvement in dual-task performance in younger and older adults. However, the extent to which training benefits to untrained tasks requires further investigation. The present study assessed (a) whether dual-task training leads to cross-modality transfer in untrained tasks using new stimuli and/or motor responses modalities, (b) whether transfer effects are related to improved ability to prepare and maintain multiple task-set and/or enhanced response coordination, (c) whether there are age-related differences in transfer effects. Twenty-three younger and 23 older adults were randomly assigned to dual-task training or control conditions. All participants were assessed before and after training on three dual-task transfer conditions; (1) stimulus modality transfer (2) response modality transfer (3) stimulus and response modalities transfer task. Training group showed larger improvement than the control group in the three transfer dual-task conditions, which suggests that training leads to more than specific learning of stimuli/response associations. Attentional costs analyses showed that training led to improved dual-task cost, only in conditions that involved new stimuli or response modalities, but not both. Moreover, training did not lead to a reduced task-set cost in the transfer conditions, which suggests some limitations in transfer effects that can be expected. Overall, the present study supports the notion that cognitive plasticity for attentional control is preserved in late adulthood.
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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".