Working memory training in healthy young adults: Support for the null from a randomized comparison to active and passive control groups
Bibliographic record
Abstract
Training of working memory as a method of increasing working memory capacity and fluid intelligence has received much attention in recent years. This burgeoning field remains highly controversial with empirically-backed disagreements at all levels of evidence, including individual studies, systematic reviews, and even meta-analyses. The current study investigated the effect of a randomized six week online working memory intervention on untrained cognitive abilities in a community-recruited sample of healthy young adults, in relation to both a processing speed training active control condition, as well as a no-contact control condition. Results of traditional null hypothesis significance testing, as well as Bayesian factor analyses, revealed support for the null hypothesis across all cognitive tests administered before and after training. Importantly, all three groups were similar at pre-training for a variety of individual variables purported to moderate transfer of training to fluid intelligence, including personality traits, motivation to train, and expectations of cognitive improvement from training. Because these results are consistent with experimental trials of equal or greater methodological rigor, we suggest that future research re-focus on: 1) other promising interventions known to increase memory performance in healthy young adults, and; 2) examining sub-populations or alternative populations in which working memory training may be efficacious.
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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 | 0.044 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".