Speech Processing as a Far-Transfer Gauge of Serious Games for Cognitive Training in Aging: Randomized Controlled Trial of Web-Based Effectivate Training
Bibliographic record
Abstract
Background The number of serious games for cognitive training in aging (SGCTAs) is proliferating in the market and attempting to combat one of the most feared aspects of aging—cognitive decline. However, the efficacy of many SGCTAs is still questionable. Even the measures used to validate SGCTAs are up for debate, with most studies using cognitive measures that gauge improvement in trained tasks, also known as near transfer. This study takes a different approach, testing the efficacy of the SGCTA—Effectivate—in generating tangible far-transfer improvements in a nontrained task—the Eye tracking of Word Identification in Noise Under Memory Increased Load (E-WINDMIL)—which tests speech processing in adverse conditions. Objective This study aimed to validate the use of a real-time measure of speech processing as a gauge of the far-transfer efficacy of an SGCTA designed to train executive functions. Methods In a randomized controlled trial that included 40 participants, we tested 20 (50%) older adults before and after self-administering the SGCTA Effectivate training and compared their performance with that of the control group of 20 (50%) older adults. The E-WINDMIL eye-tracking task was administered to all participants by blinded experimenters in 2 sessions separated by 2 to 8 weeks. Results Specifically, we tested the change between sessions in the efficiency of segregating the spoken target word from its sound-sharing alternative, as the word unfolds in time. We found that training with the SGCTA Effectivate improved both early and late speech processing in adverse conditions, with higher discrimination scores in the training group than in the control group (early processing: F1,38=7.371; P=.01; ηp2=0.162 and late processing: F1,38=9.003; P=.005; ηp2=0.192). Conclusions This study found the E-WINDMIL measure of speech processing to be a valid gauge for the far-transfer effects of executive function training. As the SGCTA Effectivate does not train any auditory task or language processing, our results provide preliminary support for the ability of Effectivate to create a generalized cognitive improvement. Given the crucial role of speech processing in healthy and successful aging, we encourage researchers and developers to use speech processing measures, the E-WINDMIL in particular, to gauge the efficacy of SGCTAs. We advocate for increased industry-wide adoption of far-transfer metrics to gauge SGCTAs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".