Using Virtual Reality to Assess and Promote Transfer of Memory Training in Older Adults With Memory Complaints: A Randomized Controlled Trial
Bibliographic record
Abstract
In this proof-of-concept study, we assessed the potential for immersive virtual reality (VR) to measure transfer following strategic memory training, and whether efficacy and transfer are increased when training is complemented by practice in an immersive virtual environment. Forty older adults with subjective memory complaints were trained with the method of loci. They were randomized to either a condition where they practiced the strategy in VR (n = 20) or a control condition where they were familiarized with VR using a non-memory task (n = 20). Training efficacy was measured with word recall, and transfer of the training benefit was measured with a recall task completed in two VR tasks (primary outcomes) as well as a self-report memory questionnaire (secondary outcomes). Testing was administered before (PRE), midway (POST 3), and after (POST 6) training. Participants improved their scores on word recall. Regarding transfer measures, participants improved their performance in the two VR recall tasks but not on the self-report memory questionnaire. No significant group effect was observed. Improvement was found when comparing PRE to POST 3 with no further improvement at POST 6. Thus, strategic memory training improved the memory of seniors with memory complaints on word recall and a transfer task relying on a VR scenario that resembles real-life. However, no evidence supporting an increase in transfer effects was found when enriching training with VR memory exercises.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".