Few Effects of a 5-week Computerized Cognitive Training Program in Healthy Older Adults
Bibliographic record
Abstract
Abstract Computerized cognitive training programs are becoming increasingly popular and practical for cognitive aging. Nevertheless, basic questions remain about the benefits of such programs, and about the degree to which participant expectations might influence training and transfer. Here we examined a commercial cognitive training program ( Activate ) in a 5-week double-blind, pseudo-randomized placebo-controlled trial. Based on a priori power analysis, we recruited 99 healthy older adults 59-91 years of age (M = 68.87, SD = 6.31; 69 women), assigning them to either the intervention or an active control program (Sudoku and n-back working memory exercises). We subdivided both groups into high and low expectation priming conditions, to probe for effects of participants’ expectations on training and transfer. We assessed transfer using a battery of standard neuropsychological and psychosocial measures that had been agreed to by the training program developers. We planned and pre-registered our analyses (on osf.io). The majority (88%) of participants progressed through the training, and most provided positive feedback about it. Similarly, the majority (80%) of participants believed they were truly training their brains. Yet, transfer of training was minimal. Also minimal were any effects of expectations on training and transfer, although participants who received high expectation priming tended to engage more with their assigned program overall. Our findings suggest limited benefits of Activate training on cognition and psychosocial wellbeing in healthy older adults, at least under the conditions we used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".