EFFECTS OF ATTENTIONAL CONTROL DEMANDS IN PROCESSING SPEED TRAINING ACROSS THE ADULT LIFESPAN: FIRST FINDINGS
Bibliographic record
Abstract
Abstract Evidence for cognitive training-induced far transfer of improvements to untrained cognitive abilities is mixed. One notable exception appears to be training interventions that target speed of processing. However, the mechanisms underpinning these training effects are yet unclear. In this pre-registered, multi-site training study, we tested the hypotheses that (a) training tasks with stronger attentional control demands will induce larger transfer effects, and that (b) gains in the rate of information accumulation (i.e., drift rate) will be positively associated with these effects. For this purpose, we recruited 476 healthy participants spanning the adult lifespan (18-85 years) from three sites in the United Kingdom, Germany, and Canada, who were randomly allocated to one of four groups practising tasks with increasing attentional control demands. Transfer to working memory, executive functions, reasoning, and everyday cognitive functioning was assessed before, immediately after, and 3 months after 10 training sessions. N = 388 participants (age in years M = 48.61, SD = 18.28, range 18 – 85; 218 women, 168 men, 2 participants with undisclosed gender; education in years M = 16.68, SD = 3.68) completed the study. The first results from this study will be presented in this talk.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".