Brain training habits are not associated with generalized benefits to cognition: An online study of over 1000 “brain trainers”.
Bibliographic record
Abstract
The foundational tenet of brain training is that general cognitive functioning can be enhanced by completing computerized games, a notion that is both intuitive and appealing. Moreover, there is strong incentive to improve our cognitive abilities, so much so that it has driven a billion-dollar industry. However, whether brain training can really produce these desired outcomes continues to be debated. This is, in part, because the literature is replete with studies that use ill-defined criteria for establishing transferable improvements to cognition, often using single training and outcome measures with small samples. To overcome these limitations, we conducted a large-scale online study to examine whether practices and beliefs about brain training are associated with better cognition. We recruited a diverse sample of over 1000 participants, who had been using an assortment of brain training programs for up to 5 years. Cognition was assessed using multiple tests that measure attention, reasoning, working memory and planning. We found no association between any measure of cognitive functioning and whether participants were currently "brain training" or not, even for the most committed brain trainers. Duration of brain training also showed no relationship with any cognitive performance measure. This result was the same regardless of participant age, which brain training program they used, or whether they expected brain training to work. Our results pose a significant challenge for "brain training" programs that purport to improve general cognitive functioning among the general population. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".